Pub Date : 2002-12-10DOI: 10.1109/ICPR.2002.1048325
P. Perner, Horst Perner, Bernd Müller
We investigated the Boolean model for the classification of textures. We were interested in three issues: 1. What are the best features for classification? 2. How does the number of Boolean models created from the original image influence the accuracy of the classifier? 3. Is decision tree induction the right method for classification? We are working on a real-world application which is the classification of HEp-2 cells. This kind of cells are used in medicine for the identification of antinuclear autoantibodies. Human experts describe the characteristics of these cells by symbolic texture features. We apply the Boolean model to this problem and assume that the primary grains are regions of random size and shape. We use decision tree induction in order to learn the relevant classification knowledge and the structure of the classifier.
{"title":"Texture classification based on the Boolean model and its application to HEp-2 cells","authors":"P. Perner, Horst Perner, Bernd Müller","doi":"10.1109/ICPR.2002.1048325","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048325","url":null,"abstract":"We investigated the Boolean model for the classification of textures. We were interested in three issues: 1. What are the best features for classification? 2. How does the number of Boolean models created from the original image influence the accuracy of the classifier? 3. Is decision tree induction the right method for classification? We are working on a real-world application which is the classification of HEp-2 cells. This kind of cells are used in medicine for the identification of antinuclear autoantibodies. Human experts describe the characteristics of these cells by symbolic texture features. We apply the Boolean model to this problem and assume that the primary grains are regions of random size and shape. We use decision tree induction in order to learn the relevant classification knowledge and the structure of the classifier.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267691","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048318
S. Marcel, Samy Bengio
The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. We propose to use an additional feature of the face image: the skin color The new feature set is tested on a benchmark database, namely XM2VTS, using a simple discriminant artificial neural network. Results show that the skin color information improves the performance.
{"title":"Improving face verification using skin color information","authors":"S. Marcel, Samy Bengio","doi":"10.1109/ICPR.2002.1048318","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048318","url":null,"abstract":"The performance of face verification systems has steadily improved over the last few years, mainly focusing on models rather than on feature processing. State-of-the-art methods often use the gray-scale face image as input. We propose to use an additional feature of the face image: the skin color The new feature set is tested on a benchmark database, namely XM2VTS, using a simple discriminant artificial neural network. Results show that the skin color information improves the performance.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126287026","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048389
T. Abe, Y. Matsuzawa
To extract object regions from images, the methods using region-based active contour model (ACM) have been proposed. By controlling ACM with the statistical characteristics of the image properties, these methods effect robust region extraction. However the existing methods require redundant processing and cannot adapt to complex scene images. To overcome these problems, we propose a new method for controlling region-based ACM. In the proposed method, a definite area is set along an object boundary. This area is partitioned into several subareas, and they, are iteratively deformed to make the image properties be uniform in each subarea. As a result of this clustering on the definite area, the image properties in a necessary and sufficient area can be effectively reflected on ACM control, and efficient and accurate region extraction can be achieved.
{"title":"Clustering-based control of active contour model","authors":"T. Abe, Y. Matsuzawa","doi":"10.1109/ICPR.2002.1048389","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048389","url":null,"abstract":"To extract object regions from images, the methods using region-based active contour model (ACM) have been proposed. By controlling ACM with the statistical characteristics of the image properties, these methods effect robust region extraction. However the existing methods require redundant processing and cannot adapt to complex scene images. To overcome these problems, we propose a new method for controlling region-based ACM. In the proposed method, a definite area is set along an object boundary. This area is partitioned into several subareas, and they, are iteratively deformed to make the image properties be uniform in each subarea. As a result of this clustering on the definite area, the image properties in a necessary and sufficient area can be effectively reflected on ACM control, and efficient and accurate region extraction can be achieved.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125675230","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048265
D. Rivière, J. F. Mangin, Jean-Marc Martinez, F. Tupin, D. Papadopoulos-Orfanos, V. Frouin
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road network on radar satellite images, and recognition of the cortical sulci on MRI images. Features must be initially extracted from the data to build a "feature graph" with structural relations. The goal is to endow each feature with a label representing either a specific object (recognition), or a class of objects (detection). Some contextual constraints have to be respected during this labelling. They are modelled by Markovian potentials assigned to the labellings of "feature clusters". The solution of the labelling problem is the minimum of the energy defined by the sum of the local potentials. This paper develops a method for learning these local potentials using "congregation" of neural networks and supervised learning.
{"title":"Relational graph labelling using learning techniques and Markov random fields","authors":"D. Rivière, J. F. Mangin, Jean-Marc Martinez, F. Tupin, D. Papadopoulos-Orfanos, V. Frouin","doi":"10.1109/ICPR.2002.1048265","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048265","url":null,"abstract":"This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road network on radar satellite images, and recognition of the cortical sulci on MRI images. Features must be initially extracted from the data to build a \"feature graph\" with structural relations. The goal is to endow each feature with a label representing either a specific object (recognition), or a class of objects (detection). Some contextual constraints have to be respected during this labelling. They are modelled by Markovian potentials assigned to the labellings of \"feature clusters\". The solution of the labelling problem is the minimum of the energy defined by the sum of the local potentials. This paper develops a method for learning these local potentials using \"congregation\" of neural networks and supervised learning.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129455432","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048337
S. Mahmoudi, M. Daoudi
In this work we introduce a new method for indexing 3D models. This method is based on the characterization of 3D objects by a set of 7 characteristic views, including three principals, and four secondaries. The primary, secondary, and tertiary viewing directions are determined by the eigenvector analysis of the covariance matrix related to the 3D object. The secondary views are deduced from the principal views. We propose an index based on "curvature scale space", organized around a tree structure, named M-Tree, which is parameterized by a distance function and allows one to considerably decrease the calculating time by saving the intermediate distances.
{"title":"3D models retrieval by using characteristic views","authors":"S. Mahmoudi, M. Daoudi","doi":"10.1109/ICPR.2002.1048337","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048337","url":null,"abstract":"In this work we introduce a new method for indexing 3D models. This method is based on the characterization of 3D objects by a set of 7 characteristic views, including three principals, and four secondaries. The primary, secondary, and tertiary viewing directions are determined by the eigenvector analysis of the covariance matrix related to the 3D object. The secondary views are deduced from the principal views. We propose an index based on \"curvature scale space\", organized around a tree structure, named M-Tree, which is parameterized by a distance function and allows one to considerably decrease the calculating time by saving the intermediate distances.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131301697","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048463
M. Naphade, S. Basu, John R. Smith, Ching-Yung Lin, Belle L. Tseng
Statistical: modeling for content based retrieval is examined in the context of recent TREC Video benchmark exercise. The TREC Video exercise can be viewed as a test bed for evaluation and comparison of a variety of different algorithms on a set of high-level queries for multimedia retrieval. We report on the use of techniques adopted from statistical learning theory. Our method depends on training of models based on large data sets. Particularly, we use statistical models such as Gaussian mixture models to build computational representations for a variety of semantic concepts including rocket-launch, outdoor greenery, sky etc. Training requires a large amount of annotated (labeled) data. Thus, we explore the use of active learning for the annotation engine that minimizes the number of training samples to be labeled for satisfactory performance.
{"title":"A statistical modeling approach to content based video retrieval","authors":"M. Naphade, S. Basu, John R. Smith, Ching-Yung Lin, Belle L. Tseng","doi":"10.1109/ICPR.2002.1048463","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048463","url":null,"abstract":"Statistical: modeling for content based retrieval is examined in the context of recent TREC Video benchmark exercise. The TREC Video exercise can be viewed as a test bed for evaluation and comparison of a variety of different algorithms on a set of high-level queries for multimedia retrieval. We report on the use of techniques adopted from statistical learning theory. Our method depends on training of models based on large data sets. Particularly, we use statistical models such as Gaussian mixture models to build computational representations for a variety of semantic concepts including rocket-launch, outdoor greenery, sky etc. Training requires a large amount of annotated (labeled) data. Thus, we explore the use of active learning for the annotation engine that minimizes the number of training samples to be labeled for satisfactory performance.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123758972","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048377
E. Michaelsen, U. Soergel, Uwe Stilla
InSAR data are used to recognise large industrial building complexes. Such buildings often show salient regular patterns of strong scatterers on their roofs. A previous segmentation which uses the intensity, height and coherence information extracts building cues. Strong scatterers are filtered by a spot detector and localised by a cluster formation. Strong scatterers are grouped in rows by a process that uses the contours of the building cues as context. Stich buildings are labelled as industrial buildings and serve as seeds to assemble adjacent buildings into complex structured building aggregates. The structure of the grouping process is depicted by a production net.
{"title":"Grouping salient scatterers in InSAR data for recognition of industrial buildings","authors":"E. Michaelsen, U. Soergel, Uwe Stilla","doi":"10.1109/ICPR.2002.1048377","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048377","url":null,"abstract":"InSAR data are used to recognise large industrial building complexes. Such buildings often show salient regular patterns of strong scatterers on their roofs. A previous segmentation which uses the intensity, height and coherence information extracts building cues. Strong scatterers are filtered by a spot detector and localised by a cluster formation. Strong scatterers are grouped in rows by a process that uses the contours of the building cues as context. Stich buildings are labelled as industrial buildings and serve as seeds to assemble adjacent buildings into complex structured building aggregates. The structure of the grouping process is depicted by a production net.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127700388","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048260
A. Erçil, Burak Büke
When the number of objects in the training set is too small for the number of features used, most classification procedures cannot find good classification boundaries. In this paper, we introduce a new technique to solve the one class classification problem based on fitting an implicit polynomial surface to the point cloud of features to model the one class which we are trying to separate from the others.
{"title":"One class classification using implicit polynomial surface fitting","authors":"A. Erçil, Burak Büke","doi":"10.1109/ICPR.2002.1048260","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048260","url":null,"abstract":"When the number of objects in the training set is too small for the number of features used, most classification procedures cannot find good classification boundaries. In this paper, we introduce a new technique to solve the one class classification problem based on fitting an implicit polynomial surface to the point cloud of features to model the one class which we are trying to separate from the others.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121862131","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048259
Il-Seok Oh, Jin-Seon Lee, B. Moon
This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations used to improve chromosomes are defined and embedded in hybrid GAs. The hybridization gives two desirable effects: improving the final performance significantly and acquiring control of subset size. For the implementation reproduction by readers, we provide detailed information of GA procedure and parameter setting. Experimental results reveal that the proposed hybrid GA is superior to a classical GA and sequential search algorithms.
{"title":"Local search-embedded genetic algorithms for feature selection","authors":"Il-Seok Oh, Jin-Seon Lee, B. Moon","doi":"10.1109/ICPR.2002.1048259","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048259","url":null,"abstract":"This paper proposes a novel hybrid genetic algorithm for the feature selection. Local search operations used to improve chromosomes are defined and embedded in hybrid GAs. The hybridization gives two desirable effects: improving the final performance significantly and acquiring control of subset size. For the implementation reproduction by readers, we provide detailed information of GA procedure and parameter setting. Experimental results reveal that the proposed hybrid GA is superior to a classical GA and sequential search algorithms.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122487887","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048456
M. Loog, B. Ginneken
We propose a general iterative contextual pixel classifier for supervised image segmentation. The iterative procedure is statistically well-founded and can be considered a variation on the iterated conditional modes (ICM) of Besag (1983). Having an initial segmentation, the algorithm iteratively updates it by reclassifying every pixel, based on the original features and, additionally, contextual information. This contextual information consists of the class labels of pixels in the neighborhood of the pixel to be reclassified. Three essential differences with the original ICM are: (1) our update step is merely based on a classification result, hence a voiding the explicit calculation of conditional probabilities; (2) the clique formalism of the Markov random field framework is not required; (3) no assumption is made w.r.t. the conditional independence of the observed pixel values given the segmented image. The important consequence of properties 1 and 2 is that one can easily incorporate rate common pattern recognition tools in our segmentation algorithm. Examples are different classifiers-e.g. Fisher linear discriminant, nearest-neighbor classifier, or support vector machines-and dimension reduction techniques like LDA, or PCA. We experimentally compare a specific instance of our general method to pixel classification, using simulated data and chest radiographs, and show that the former outperforms the latter.
{"title":"Supervised segmentation by iterated contextual pixel classification","authors":"M. Loog, B. Ginneken","doi":"10.1109/ICPR.2002.1048456","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048456","url":null,"abstract":"We propose a general iterative contextual pixel classifier for supervised image segmentation. The iterative procedure is statistically well-founded and can be considered a variation on the iterated conditional modes (ICM) of Besag (1983). Having an initial segmentation, the algorithm iteratively updates it by reclassifying every pixel, based on the original features and, additionally, contextual information. This contextual information consists of the class labels of pixels in the neighborhood of the pixel to be reclassified. Three essential differences with the original ICM are: (1) our update step is merely based on a classification result, hence a voiding the explicit calculation of conditional probabilities; (2) the clique formalism of the Markov random field framework is not required; (3) no assumption is made w.r.t. the conditional independence of the observed pixel values given the segmented image. The important consequence of properties 1 and 2 is that one can easily incorporate rate common pattern recognition tools in our segmentation algorithm. Examples are different classifiers-e.g. Fisher linear discriminant, nearest-neighbor classifier, or support vector machines-and dimension reduction techniques like LDA, or PCA. We experimentally compare a specific instance of our general method to pixel classification, using simulated data and chest radiographs, and show that the former outperforms the latter.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188967","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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