Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9413203
Federico Tomasi, Veronica Tozzo, A. Barla
Graphical models allow to describe the interplay among variables of a system through a compact representation, suitable when relations evolve over time. For example, in a biological setting, genes interact differently depending on external environmental or metabolic factors. To incorporate this dynamics a viable strategy is to estimate a sequence of temporally related graphs assuming similarity among samples in different time points. While adjacent time points may direct the analysis towards a robust estimate of the underlying graph, the resulting model will not incorporate long-term or recurrent temporal relationships. In this work we propose a dynamical network inference model that leverages on kernels to consider general temporal patterns (such as circadian rhythms or seasonality). We show how our approach may also be exploited when the recurrent patterns are unknown, by coupling the network inference with a clustering procedure that detects possibly non-consecutive similar networks. Such clusters are then used to build similarity kernels. The convexity of the functional is determined by whether we impose or infer the kernel. In the first case, the optimisation algorithm exploits efficiently proximity operators with closed-form solutions. In the other case, we resort to an alternating minimisation procedure which jointly learns the temporal kernel and the underlying network. Extensive analysis on synthetic data shows the efficacy of our models compared to state-of-the-art methods. Finally, we applied our approach on two realworld applications to show how considering long-term patterns is fundamental to have insights on the behaviour of a complex system.
{"title":"Temporal Pattern Detection in Time-Varying Graphical Models","authors":"Federico Tomasi, Veronica Tozzo, A. Barla","doi":"10.1109/ICPR48806.2021.9413203","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9413203","url":null,"abstract":"Graphical models allow to describe the interplay among variables of a system through a compact representation, suitable when relations evolve over time. For example, in a biological setting, genes interact differently depending on external environmental or metabolic factors. To incorporate this dynamics a viable strategy is to estimate a sequence of temporally related graphs assuming similarity among samples in different time points. While adjacent time points may direct the analysis towards a robust estimate of the underlying graph, the resulting model will not incorporate long-term or recurrent temporal relationships. In this work we propose a dynamical network inference model that leverages on kernels to consider general temporal patterns (such as circadian rhythms or seasonality). We show how our approach may also be exploited when the recurrent patterns are unknown, by coupling the network inference with a clustering procedure that detects possibly non-consecutive similar networks. Such clusters are then used to build similarity kernels. The convexity of the functional is determined by whether we impose or infer the kernel. In the first case, the optimisation algorithm exploits efficiently proximity operators with closed-form solutions. In the other case, we resort to an alternating minimisation procedure which jointly learns the temporal kernel and the underlying network. Extensive analysis on synthetic data shows the efficacy of our models compared to state-of-the-art methods. Finally, we applied our approach on two realworld applications to show how considering long-term patterns is fundamental to have insights on the behaviour of a complex system.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"1993 1","pages":"4481-4488"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82388610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412516
Helmi Fraser, Sen Wang
Obtaining a meaningful, interpretable yet compact representation of the immediate surroundings of an autonomous vehicle is paramount for effective operation as well as safety. This paper proposes a solution to this by representing semantically important objects from a top-down, ego-centric bird's eye view. The novelty in this work is from formulating this problem as an adversarial learning task, tasking a generator model to produce bird's eye view representations which are plausible enough to be mistaken as a ground truth sample. This is achieved by using a Wasserstein Generative Adversarial Network based model conditioned on object detections from monocular RGB images and the corresponding bounding boxes. Extensive experiments show our model is more robust to novel data compared to strictly supervised benchmark models, while being a fraction of the size of the next best.
{"title":"DeepBEV: A Conditional Adversarial Network for Bird's Eye View Generation","authors":"Helmi Fraser, Sen Wang","doi":"10.1109/ICPR48806.2021.9412516","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412516","url":null,"abstract":"Obtaining a meaningful, interpretable yet compact representation of the immediate surroundings of an autonomous vehicle is paramount for effective operation as well as safety. This paper proposes a solution to this by representing semantically important objects from a top-down, ego-centric bird's eye view. The novelty in this work is from formulating this problem as an adversarial learning task, tasking a generator model to produce bird's eye view representations which are plausible enough to be mistaken as a ground truth sample. This is achieved by using a Wasserstein Generative Adversarial Network based model conditioned on object detections from monocular RGB images and the corresponding bounding boxes. Extensive experiments show our model is more robust to novel data compared to strictly supervised benchmark models, while being a fraction of the size of the next best.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"1 1","pages":"5581-5586"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82513165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9413212
M. Ai, Guozhi Shan, Bo Liu, Tianyan Liu
Person re-identification (ReID) aims to identify the specific pedestrian in a series of images or videos. Recently, ReID is receiving more and more attention in the fields of computer vision research and application like intelligent security. One major issue downgrading the ReID model performance lies in that various subjects in the same body orientations look too similar to distinguish by the model, while the same subject viewed in different orientations looks rather different. However, most of the current studies do not particularly differentiate pedestrians in orientation when designing the network, so we rethink this problem particularly from the perspective of person orientation and propose a new network structure by including two branches: one handling samples with the same body orientations and the other handling samples with different body orientations. Correspondingly, we also propose an orientation classifier that can accurately distinguish the orientation of each person. At the same time, the three-part loss functions are introduced for orientation constraint and combined to optimize the network simultaneously. Also, we use global and local features int the training stage in order to make use of multi-level information. Therefore, our network can derive its efficacy from orientation constraints and multiple features. Experiments show that our method not only has competitive performance on multiple datasets, but also can let retrieval results aligned with the orientation of the query sample rank higher, which may have great potential in the practical applications.
{"title":"Rethinking ReID: Multi-Feature Fusion Person Re-identification Based on Orientation Constraints","authors":"M. Ai, Guozhi Shan, Bo Liu, Tianyan Liu","doi":"10.1109/ICPR48806.2021.9413212","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9413212","url":null,"abstract":"Person re-identification (ReID) aims to identify the specific pedestrian in a series of images or videos. Recently, ReID is receiving more and more attention in the fields of computer vision research and application like intelligent security. One major issue downgrading the ReID model performance lies in that various subjects in the same body orientations look too similar to distinguish by the model, while the same subject viewed in different orientations looks rather different. However, most of the current studies do not particularly differentiate pedestrians in orientation when designing the network, so we rethink this problem particularly from the perspective of person orientation and propose a new network structure by including two branches: one handling samples with the same body orientations and the other handling samples with different body orientations. Correspondingly, we also propose an orientation classifier that can accurately distinguish the orientation of each person. At the same time, the three-part loss functions are introduced for orientation constraint and combined to optimize the network simultaneously. Also, we use global and local features int the training stage in order to make use of multi-level information. Therefore, our network can derive its efficacy from orientation constraints and multiple features. Experiments show that our method not only has competitive performance on multiple datasets, but also can let retrieval results aligned with the orientation of the query sample rank higher, which may have great potential in the practical applications.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"89 1","pages":"1904-1911"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82261466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9413084
Francesca Murabito, S. Palazzo, Federica Proietto Salanitri, F. Rundo, Ulas Bagci, D. Giordano, R. Leonardi, C. Spampinato
In this paper we define a deep learning architecture for automated segmentation of anatomical structures in Craniomaxillofacial (CMF) CT scans that leverages the recent success of encoder-decoder models for semantic segmentation of natural images. In particular, we propose a fully convolutional deep network that combines the advantages of recent fully convolutional models, such as Tiramisu, with squeeze-and-excitation blocks for feature recalibration, integrated with convolutional LSTMs to model spatio-temporal correlations between consecutive slices. The proposed segmentation network shows superior performance and generalization capabilities (to different structures and imaging modalities) than state of the art methods on automated segmentation of CMF structures (e.g., mandibles and airways) in several standard benchmarks (e.g., MICCAI datasets) and on new datasets proposed herein, effectively facing shape variability.
{"title":"Deep Recurrent-Convolutional Model for Automated Segmentation of Craniomaxillofacial CT Scans","authors":"Francesca Murabito, S. Palazzo, Federica Proietto Salanitri, F. Rundo, Ulas Bagci, D. Giordano, R. Leonardi, C. Spampinato","doi":"10.1109/ICPR48806.2021.9413084","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9413084","url":null,"abstract":"In this paper we define a deep learning architecture for automated segmentation of anatomical structures in Craniomaxillofacial (CMF) CT scans that leverages the recent success of encoder-decoder models for semantic segmentation of natural images. In particular, we propose a fully convolutional deep network that combines the advantages of recent fully convolutional models, such as Tiramisu, with squeeze-and-excitation blocks for feature recalibration, integrated with convolutional LSTMs to model spatio-temporal correlations between consecutive slices. The proposed segmentation network shows superior performance and generalization capabilities (to different structures and imaging modalities) than state of the art methods on automated segmentation of CMF structures (e.g., mandibles and airways) in several standard benchmarks (e.g., MICCAI datasets) and on new datasets proposed herein, effectively facing shape variability.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"7 1","pages":"9062-9067"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82359493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412368
Shaik Masihullah, Ritu Garg, Prerana Mukherjee, Anupama Ray
In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few-shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.
{"title":"Attention Based Coupled Framework for Road and Pothole Segmentation","authors":"Shaik Masihullah, Ritu Garg, Prerana Mukherjee, Anupama Ray","doi":"10.1109/ICPR48806.2021.9412368","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412368","url":null,"abstract":"In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few-shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"491 1","pages":"5812-5819"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78835217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412665
Edward Collier, S. Mukhopadhyay
Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability.
{"title":"GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks","authors":"Edward Collier, S. Mukhopadhyay","doi":"10.1109/ICPR48806.2021.9412665","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412665","url":null,"abstract":"Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"37 1","pages":"8384-8391"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78946401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412683
A. Khoder, F. Dornaika
The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multiclass classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Interclass sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix. The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.
{"title":"Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity","authors":"A. Khoder, F. Dornaika","doi":"10.1109/ICPR48806.2021.9412683","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412683","url":null,"abstract":"The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multiclass classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Interclass sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix. The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"16 1","pages":"7258-7264"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76377770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412129
Alireza Rezaei, S. L. Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, M. Malagodi
Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of necessary interventions. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn-out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the “Violins UVIFL imagery” dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with state-of-the-art clustering methods show improved overall precision and recall.
{"title":"One step clustering based on a-contrario framework for detection of alterations in historical violins","authors":"Alireza Rezaei, S. L. Hégarat-Mascle, Emanuel Aldea, Piercarlo Dondi, M. Malagodi","doi":"10.1109/ICPR48806.2021.9412129","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412129","url":null,"abstract":"Preventive conservation is an important practice in Cultural Heritage. The constant monitoring of the state of conservation of an artwork helps us reduce the risk of damage and number of necessary interventions. In this work, we propose a probabilistic approach for the detection of alterations on the surface of historical violins based on an a-contrario framework. Our method is a one step NFA clustering solution which considers grey-level and spatial density information in one background model. The proposed method is robust to noise and avoids parameter tuning and any assumption about the quantity of the worn-out areas. We have used as input UV induced fluorescence (UVIFL) images for considering details not perceivable with visible light. Tests were conducted on image sequences included in the “Violins UVIFL imagery” dataset. Results illustrate the ability of the algorithm to distinguish the worn area from the surrounding regions. Comparisons with state-of-the-art clustering methods show improved overall precision and recall.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"69 1","pages":"9348-9355"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76531316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412737
Jakub Kraśniewski, Lukasz Dabala, M. Lewandowski
Environmental protection is one of the main challenges facing society nowadays. Even with constantly growing awareness, not all of the sorting can be done by people themselves - the differences between materials are not visible to the human eye. For that reason, we present the use of a hyperspectral camera as a capture device, which allows us to obtain the full spectrum of the material. In this work we propose a method for efficient recognition of the substance of an item. We conducted several experiments and analysis of the spectra of different materials in different conditions on a special measuring stand. That enabled identification of the best features, which can later be used during classification, which was confirmed during the extensive testing procedure.
{"title":"Hyperspectral Imaging for Analysis and Classification of Plastic Waste","authors":"Jakub Kraśniewski, Lukasz Dabala, M. Lewandowski","doi":"10.1109/ICPR48806.2021.9412737","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412737","url":null,"abstract":"Environmental protection is one of the main challenges facing society nowadays. Even with constantly growing awareness, not all of the sorting can be done by people themselves - the differences between materials are not visible to the human eye. For that reason, we present the use of a hyperspectral camera as a capture device, which allows us to obtain the full spectrum of the material. In this work we propose a method for efficient recognition of the substance of an item. We conducted several experiments and analysis of the spectra of different materials in different conditions on a special measuring stand. That enabled identification of the best features, which can later be used during classification, which was confirmed during the extensive testing procedure.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"24 1","pages":"4805-4812"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76173581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-10DOI: 10.1109/ICPR48806.2021.9412892
Mohamed Chelali, Camille Kurtz, A. Puissant, N. Vincent
Satellite Image Time Series (SITS), MRI sequences, and more generally image time series, constitute $2D+t$ data providing spatial and temporal information about an observed scene. Given a pattern recognition task such as image classification, considering jointly such rich information is crucial during the decision process. Nevertheless, due to the complex representation of the data-cube, spatio-temporal features extraction from $2D+t$ data remains difficult to handle. We present in this article an approach to learn such features from this data, and then to proceed to their classification. Our strategy consists in enriching pixel time series with spatial information. It is based on Random Walk to build a novel segment-based representation of the data, passing from a $2D+t$ dimension to a $2D$ one, without loosing too much spatial information. Such new representation is then involved in an end-to-end learning process with a classical 2D Convolutional Neural Network (CNN) in order to learn spatiotemporal features for the classification of image time series. Our approach is evaluated on a remote sensing application for the mapping of agricultural crops. Thanks to a visual attention mechanism, the proposed $2D$ spatio-temporal representation makes also easier the interpretation of a SITS to understand spatiotemporal phenomenons related to soil management practices.
{"title":"Classification of spatially enriched pixel time series with convolutional neural networks","authors":"Mohamed Chelali, Camille Kurtz, A. Puissant, N. Vincent","doi":"10.1109/ICPR48806.2021.9412892","DOIUrl":"https://doi.org/10.1109/ICPR48806.2021.9412892","url":null,"abstract":"Satellite Image Time Series (SITS), MRI sequences, and more generally image time series, constitute $2D+t$ data providing spatial and temporal information about an observed scene. Given a pattern recognition task such as image classification, considering jointly such rich information is crucial during the decision process. Nevertheless, due to the complex representation of the data-cube, spatio-temporal features extraction from $2D+t$ data remains difficult to handle. We present in this article an approach to learn such features from this data, and then to proceed to their classification. Our strategy consists in enriching pixel time series with spatial information. It is based on Random Walk to build a novel segment-based representation of the data, passing from a $2D+t$ dimension to a $2D$ one, without loosing too much spatial information. Such new representation is then involved in an end-to-end learning process with a classical 2D Convolutional Neural Network (CNN) in order to learn spatiotemporal features for the classification of image time series. Our approach is evaluated on a remote sensing application for the mapping of agricultural crops. Thanks to a visual attention mechanism, the proposed $2D$ spatio-temporal representation makes also easier the interpretation of a SITS to understand spatiotemporal phenomenons related to soil management practices.","PeriodicalId":6783,"journal":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","volume":"4 1","pages":"5310-5317"},"PeriodicalIF":0.0,"publicationDate":"2021-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87487170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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