In this paper, the authors propose an effective information system that can supply food product information to meet consumer needs. Consumer awareness of food safety has recently intensified, and food allergy issues seem to be of increasing concern these days, hence the need for effective solutions are required. Improving the quality of food might be an important solution, but supplementation with key information for consumers might also be considered important. To help resolve this issue, the authors developed a prototype system to meet consumer needs based on the survey conducted. Place Engine is implemented for this system, allowing users to estimate their current location by utilizing Wi-Fi devices, and obtain information as to where they can obtain food that meets their requirements nearby.
{"title":"Food Product Information Supplement System - Corresponding to Consumer Needs for Shopping and Eating Out","authors":"Kayo H. Iizuka, Takuya Okawada, Yasuki Iizuka","doi":"10.1109/ISM.2011.72","DOIUrl":"https://doi.org/10.1109/ISM.2011.72","url":null,"abstract":"In this paper, the authors propose an effective information system that can supply food product information to meet consumer needs. Consumer awareness of food safety has recently intensified, and food allergy issues seem to be of increasing concern these days, hence the need for effective solutions are required. Improving the quality of food might be an important solution, but supplementation with key information for consumers might also be considered important. To help resolve this issue, the authors developed a prototype system to meet consumer needs based on the survey conducted. Place Engine is implemented for this system, allowing users to estimate their current location by utilizing Wi-Fi devices, and obtain information as to where they can obtain food that meets their requirements nearby.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777234","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}
To measure the quality of experience (QoE) of a video, the current approaches of objective quality metrics development focus on how to design a video quality model, which considers the effects of the extracted features and models the Human Visual System (HVS). However, video quality metrics which try to model the HVS confronts a fact that HVS is too complicated and not well understood to model. In this paper, instead of modeling the objective quality metrics with some functions, we proposed to build a video quality metrics using the support vector machines (SVMs) supervised learning. With the proposed SVM based video quality prediction, it allows a much better approximation to the NTIA-VQM and MOS values, compared to the previous G.1070-based video quality prediction. We further investigated how to choose the certain features which can be efficiently used as SVM input variables.
{"title":"Support Vector Regression Based Video Quality Prediction","authors":"Beibei Wang, D. Zou, Ran Ding","doi":"10.1109/ISM.2011.84","DOIUrl":"https://doi.org/10.1109/ISM.2011.84","url":null,"abstract":"To measure the quality of experience (QoE) of a video, the current approaches of objective quality metrics development focus on how to design a video quality model, which considers the effects of the extracted features and models the Human Visual System (HVS). However, video quality metrics which try to model the HVS confronts a fact that HVS is too complicated and not well understood to model. In this paper, instead of modeling the objective quality metrics with some functions, we proposed to build a video quality metrics using the support vector machines (SVMs) supervised learning. With the proposed SVM based video quality prediction, it allows a much better approximation to the NTIA-VQM and MOS values, compared to the previous G.1070-based video quality prediction. We further investigated how to choose the certain features which can be efficiently used as SVM input variables.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116938414","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}
Special cameras such as 3D scanners or depth cameras are necessary in recognizing 3D shapes from input faces. In this paper, we propose an efficient face fitting method which is able to fit various faces including any variations of 3D poses (the rotation of X, Y axes) and facial expressions. Our method takes an advantage of 2D Active Appearance Models (AAM) from 2D face images rather than using the depth information measured by special cameras. We first construct an AAM for the variations of the facial expression. Then, we estimate depth information of each land-mark from frontal and side view images. By combining the estimated depth information with AAM, we can fit various 3D transformed faces. Self-occlusions due to the 3D pose variation are also processed by the region weighting function on the normalized face at each frame. Our experimental results show that the proposed method can efficiently fit various faces better than the typical AAM and View-based AAM.
{"title":"3D Face Fitting Method Based on 2D Active Appearance Models","authors":"Myung-Ho Ju, Hang-Bong Kang","doi":"10.1109/ISM.2011.11","DOIUrl":"https://doi.org/10.1109/ISM.2011.11","url":null,"abstract":"Special cameras such as 3D scanners or depth cameras are necessary in recognizing 3D shapes from input faces. In this paper, we propose an efficient face fitting method which is able to fit various faces including any variations of 3D poses (the rotation of X, Y axes) and facial expressions. Our method takes an advantage of 2D Active Appearance Models (AAM) from 2D face images rather than using the depth information measured by special cameras. We first construct an AAM for the variations of the facial expression. Then, we estimate depth information of each land-mark from frontal and side view images. By combining the estimated depth information with AAM, we can fit various 3D transformed faces. Self-occlusions due to the 3D pose variation are also processed by the region weighting function on the normalized face at each frame. Our experimental results show that the proposed method can efficiently fit various faces better than the typical AAM and View-based AAM.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120951882","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}
The Local Binary pattern (LBP) is a well-known feature and has been widely used for human identification. However, the amount of information extracted is limited which reduces the LBP discriminative power. Recently, some enhancements have been proposed by adding preprocessing stages or considering more neighbor pixels to enrich the extracted feature. In this paper, we propose Uniformly-sampled Thresholds for LBP (UTLBP) operator that increases the richness of information derived from the LBP feature. It outperforms other features in various probe sets of the large CAS-PEAL database for face recognition. Moreover, we collected a database of 25 families to verify the superiority of the proposed feature in the family verification. Results show that using the UTLBP, the total error in face recognition and family verification is reduced up to 8% and 3% respectively comparing to the state of the art LBP. It improves the missing family member verification performance up to 3% where, contrary to expectation, increasing the LBP operator radius worsens the performance by 2%.
{"title":"Enhancing Local Binary Patterns Distinctiveness for Face Representation","authors":"M. Ghahramani, W. Yau, E. Teoh","doi":"10.1109/ISM.2011.78","DOIUrl":"https://doi.org/10.1109/ISM.2011.78","url":null,"abstract":"The Local Binary pattern (LBP) is a well-known feature and has been widely used for human identification. However, the amount of information extracted is limited which reduces the LBP discriminative power. Recently, some enhancements have been proposed by adding preprocessing stages or considering more neighbor pixels to enrich the extracted feature. In this paper, we propose Uniformly-sampled Thresholds for LBP (UTLBP) operator that increases the richness of information derived from the LBP feature. It outperforms other features in various probe sets of the large CAS-PEAL database for face recognition. Moreover, we collected a database of 25 families to verify the superiority of the proposed feature in the family verification. Results show that using the UTLBP, the total error in face recognition and family verification is reduced up to 8% and 3% respectively comparing to the state of the art LBP. It improves the missing family member verification performance up to 3% where, contrary to expectation, increasing the LBP operator radius worsens the performance by 2%.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124832920","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}
Video-telephony and mobile TV are typical multimedia services which are becoming a part of the everyday life due to the increase in bandwidth availability and also viewing devices with larger screen sizes (smart phone, PDA, etc). To ensure high quality, packet layer parametric quality prediction models for audio-visual services like video-telephony and IPTV video streaming have emerged and are still under development. Those parametric models depend on a set of parameters which have to be tuned for every specific application. In this work, we carry out an experiment to analyze the impact of video resolution and up scaling operation on perceived quality. We could show that the current parametric models can be modified to explicitly integrate the joined effect of resolution and display video size.
{"title":"An Approach for Modeling the Effects of Video Resolution and Size on the Perceived Visual Quality","authors":"Benjamin Belmudez, S. Möller","doi":"10.1109/ISM.2011.82","DOIUrl":"https://doi.org/10.1109/ISM.2011.82","url":null,"abstract":"Video-telephony and mobile TV are typical multimedia services which are becoming a part of the everyday life due to the increase in bandwidth availability and also viewing devices with larger screen sizes (smart phone, PDA, etc). To ensure high quality, packet layer parametric quality prediction models for audio-visual services like video-telephony and IPTV video streaming have emerged and are still under development. Those parametric models depend on a set of parameters which have to be tuned for every specific application. In this work, we carry out an experiment to analyze the impact of video resolution and up scaling operation on perceived quality. We could show that the current parametric models can be modified to explicitly integrate the joined effect of resolution and display video size.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660606","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}
The HP Fan Camera is a panoramic mosaic king camera that is a composite of 24-imager array system. Streaming the captured video is a challenging problem due to several factors such as the large bandwidth requirements, the limited capabilities of the client's machines, and our desire to provide independent viewing controls for users. In the process of developing an optimal rate controller for the HP Fan Camera we developed a client-server framework for multi-camera streaming and performed a set of experiments using various bandwidth allocation schemes. From our preliminary research, we found that sending individual streams of the cameras over the network provides more interactivity to the end users and requires less bandwidth in case the behavior of the end users is aggressive in scene selection. In this paper we present this framework and share the results of our conducted experiments.
{"title":"The Mosaic Camera: Streaming, Coding and Compositing Experiments","authors":"Mashhour Solh, G. Al-Regib","doi":"10.1109/ISM.2011.106","DOIUrl":"https://doi.org/10.1109/ISM.2011.106","url":null,"abstract":"The HP Fan Camera is a panoramic mosaic king camera that is a composite of 24-imager array system. Streaming the captured video is a challenging problem due to several factors such as the large bandwidth requirements, the limited capabilities of the client's machines, and our desire to provide independent viewing controls for users. In the process of developing an optimal rate controller for the HP Fan Camera we developed a client-server framework for multi-camera streaming and performed a set of experiments using various bandwidth allocation schemes. From our preliminary research, we found that sending individual streams of the cameras over the network provides more interactivity to the end users and requires less bandwidth in case the behavior of the end users is aggressive in scene selection. In this paper we present this framework and share the results of our conducted experiments.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123919631","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}
This paper addresses the problem of concept learning for semantic image retrieval. Two types of semantic concepts are introduced in our system: the individual concept and the scene concept. The individual concepts are explicitly provided in a vocabulary of semantic words, which are the labels or annotations in an image database. Scene concepts are higher level concepts which are defined as potential patterns of co occurrence of individual concepts. Scene concepts exist since some of the individual concepts co-occur frequently across different images. This is similar to human learning where understanding of simpler ideas is generally useful prior to developing more sophisticated ones. Scene concepts can have more discriminative power compared to individual concepts but methods are needed to find them. A novel method for deriving scene concepts is presented. It is based on a weighted concept co-occurrence network (graph) with detected community structure property. An image similarity comparison and retrieval framework is described with the proposed individual and scene concept signature as the image semantic descriptors. Extensive experiments are conducted on a publicly available dataset to demonstrate the effectiveness of our concept learning and semantic image retrieval framework.
{"title":"Concept Learning with Co-occurrence Network for Image Retrieval","authors":"Linan Feng, B. Bhanu","doi":"10.1109/ISM.2011.77","DOIUrl":"https://doi.org/10.1109/ISM.2011.77","url":null,"abstract":"This paper addresses the problem of concept learning for semantic image retrieval. Two types of semantic concepts are introduced in our system: the individual concept and the scene concept. The individual concepts are explicitly provided in a vocabulary of semantic words, which are the labels or annotations in an image database. Scene concepts are higher level concepts which are defined as potential patterns of co occurrence of individual concepts. Scene concepts exist since some of the individual concepts co-occur frequently across different images. This is similar to human learning where understanding of simpler ideas is generally useful prior to developing more sophisticated ones. Scene concepts can have more discriminative power compared to individual concepts but methods are needed to find them. A novel method for deriving scene concepts is presented. It is based on a weighted concept co-occurrence network (graph) with detected community structure property. An image similarity comparison and retrieval framework is described with the proposed individual and scene concept signature as the image semantic descriptors. Extensive experiments are conducted on a publicly available dataset to demonstrate the effectiveness of our concept learning and semantic image retrieval framework.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115829278","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}
R. Mertens, Po-Sen Huang, L. Gottlieb, G. Friedland, Ajay Divakaran
Recently, audio concepts emerged as a useful building block in multimodal video retrieval systems. Information like "this file contains laughter", "this file contains engine sounds" or "this file contains slow music" can significantly improve purely visual based retrieval. The weak point of current approaches to audio concept detection is that they heavily rely on human annotators. In most approaches, audio material is manually inspected to identify relevant concepts. Then instances that contain examples of relevant concepts are selected -- again manually -- and used to train concept detectors. This approach comes with two major disadvantages: (1) it leads to rather abstract audio concepts that hardly cover the audio domain at hand and (2) the way human annotators identify audio concepts likely differs from the way a computer algorithm clusters audio data -- introducing additional noise in training data. This paper explores whether unsupervized audio segementation systems can be used to identify useful audio concepts by analyzing training data automatically and whether these audio concepts can be used for multimedia document classification and retrieval. A modified version of the ICSI (International Computer Science Institute) speaker diarization system finds segments in an audio track that have similar perceptual properties and groups these segments. This article provides an in-depth analysis on the statistic properties of similar acoustic segments identified by the diarization system in a predefined document set and the theoretical fitness of this approach to discern one document class from another.
{"title":"On the Applicability of Speaker Diarization to Audio Concept Detection for Multimedia Retrieval","authors":"R. Mertens, Po-Sen Huang, L. Gottlieb, G. Friedland, Ajay Divakaran","doi":"10.1109/ISM.2011.79","DOIUrl":"https://doi.org/10.1109/ISM.2011.79","url":null,"abstract":"Recently, audio concepts emerged as a useful building block in multimodal video retrieval systems. Information like \"this file contains laughter\", \"this file contains engine sounds\" or \"this file contains slow music\" can significantly improve purely visual based retrieval. The weak point of current approaches to audio concept detection is that they heavily rely on human annotators. In most approaches, audio material is manually inspected to identify relevant concepts. Then instances that contain examples of relevant concepts are selected -- again manually -- and used to train concept detectors. This approach comes with two major disadvantages: (1) it leads to rather abstract audio concepts that hardly cover the audio domain at hand and (2) the way human annotators identify audio concepts likely differs from the way a computer algorithm clusters audio data -- introducing additional noise in training data. This paper explores whether unsupervized audio segementation systems can be used to identify useful audio concepts by analyzing training data automatically and whether these audio concepts can be used for multimedia document classification and retrieval. A modified version of the ICSI (International Computer Science Institute) speaker diarization system finds segments in an audio track that have similar perceptual properties and groups these segments. This article provides an in-depth analysis on the statistic properties of similar acoustic segments identified by the diarization system in a predefined document set and the theoretical fitness of this approach to discern one document class from another.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131597263","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}
T. Hossfeld, Michael Seufert, Matthias Hirth, T. Zinner, P. Tran-Gia, R. Schatz
This paper addresses the challenge of assessing and modeling Quality of Experience (QoE) for online video services that are based on TCP-streaming. We present a dedicated QoE model for You Tube that takes into account the key influence factors (such as stalling events caused by network bottlenecks) that shape quality perception of this service. As second contribution, we propose a generic subjective QoE assessment methodology for multimedia applications (like online video) that is based on crowd sourcing - a highly cost-efficient, fast and flexible way of conducting user experiments. We demonstrate how our approach successfully leverages the inherent strengths of crowd sourcing while addressing critical aspects such as the reliability of the experimental data obtained. Our results suggest that, crowd sourcing is a highly effective QoE assessment method not only for online video, but also for a wide range of other current and future Internet applications.
{"title":"Quantification of YouTube QoE via Crowdsourcing","authors":"T. Hossfeld, Michael Seufert, Matthias Hirth, T. Zinner, P. Tran-Gia, R. Schatz","doi":"10.1109/ISM.2011.87","DOIUrl":"https://doi.org/10.1109/ISM.2011.87","url":null,"abstract":"This paper addresses the challenge of assessing and modeling Quality of Experience (QoE) for online video services that are based on TCP-streaming. We present a dedicated QoE model for You Tube that takes into account the key influence factors (such as stalling events caused by network bottlenecks) that shape quality perception of this service. As second contribution, we propose a generic subjective QoE assessment methodology for multimedia applications (like online video) that is based on crowd sourcing - a highly cost-efficient, fast and flexible way of conducting user experiments. We demonstrate how our approach successfully leverages the inherent strengths of crowd sourcing while addressing critical aspects such as the reliability of the experimental data obtained. Our results suggest that, crowd sourcing is a highly effective QoE assessment method not only for online video, but also for a wide range of other current and future Internet applications.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133440807","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}
Alina Elma Abduraman, Sid-Ahmed Berrani, B. Mérialdo
This paper addresses the problem of unsupervised TV programs structuring. Program structuring allows direct and non linear access to the desired parts of a program. Our work addresses the structuring of recurrent TV programs like news, entertainment programs, TV shows, TV magazines. In a previous work we proposed a program structuring method based on the detection of video recurrences. In this paper we extend our study to audio recurrences and verify their influence on the final structuring. We evaluate the structuring results on both approaches (audio and video) separately and jointly. We use for evaluation a 62 hours dataset corresponding to 97 episodes of TV programs.
{"title":"Audio Recurrence Contribution to a Video-based TV Program Structuring Approach","authors":"Alina Elma Abduraman, Sid-Ahmed Berrani, B. Mérialdo","doi":"10.1109/ISM.2011.15","DOIUrl":"https://doi.org/10.1109/ISM.2011.15","url":null,"abstract":"This paper addresses the problem of unsupervised TV programs structuring. Program structuring allows direct and non linear access to the desired parts of a program. Our work addresses the structuring of recurrent TV programs like news, entertainment programs, TV shows, TV magazines. In a previous work we proposed a program structuring method based on the detection of video recurrences. In this paper we extend our study to audio recurrences and verify their influence on the final structuring. We evaluate the structuring results on both approaches (audio and video) separately and jointly. We use for evaluation a 62 hours dataset corresponding to 97 episodes of TV programs.","PeriodicalId":339410,"journal":{"name":"2011 IEEE International Symposium on Multimedia","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133223826","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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