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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, CIMSVP"

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Using fuzzy rough feature selection for image retrieval system

Using fuzzy rough feature selection for image retrieval syst...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Maryam Shahabi Lotfabadi Mohd Fairuz Shiratuddin Kok Wai Wong School of Information Technology Murdoch University Murdoch Australia

Feature selection is an important step in processing the images especially for applications such as content based image retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve similar images from a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. Thus feature selection is an important step. Fuzzy rough feature selection method has many advantages in determining the relevant features. In this paper, five feature selection methods are compared with the fuzzy rough method. These five feature selection methods are Relief-F, Information Gain, Gain Ratio, OneR and the statistical measure χ 2 . The main purpose of the comparison is to rank the image features and see which method provides better results. An image retrieval dataset (COREL dataset) was used in the comparison. In order to evaluate the performance of the six methods, ranking of the important features is defined. This is then used to compare with the automated ranking produced by the aforesaid feature selection methods. Results show that the retrieval system using fuzzy rough feature selection has better retrieval accuracy and provide good Precision Recall performance. The advantages of the use of fuzzy rough feature selection will also be discussed in the paper.

关键词： Image retrieval Accuracy Entropy Feature extraction multimedia communication Gain measurement Vectors

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Mining visual complexity of images based on an enhanced feature space representation

Mining visual complexity of images based on an enhanced feat...

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ieee International symposium on Intelligent signal processing

作者： Abdullah M. Iliyasu Awad Kh. Al-Asmari Mohamed A. AbdelWahab Ahmed S. Salama Mohamed A. Al-Qodah Asif R. Khan Phuc Q. Le Fei Yan Department of Computational Intelligence & Systems Science Tokyo Institute of Technology Japan Salman Bin Abdul-Aziz University Al Kharj Kingdom of Saudi Arabia Image and Signal Processing Lab King Saud University Kingdom of Saudi Arabia

An enhanced feature space to represent visual complexity of images, as would the HVS, is presented. Specifically, the ratio between the coherent and incoherent pixels in an image was used as a measure of the chromatic contributions to the visual complexity of an image. Similarly, the contrast, energy, entropy and homogeneity were modelled as the textural attributes of an image's visual complexity. Integrated into the SND feature space, these new (chromatic and textural) features facilitate a better and enhanced representation of visual complexity. Using the Corel 1000A dataset to validate the veracity of the proposal, the enhanced visual complexity space, the SND + space, improves the capability to better represent visual complexity by a 16.7% increase in the exact correlation with a subjective (human) evaluation of the same dataset over the original SND space. Pursued further, the effective representation of visual complexity would have profound impacts in many areas of image processing and computer vision.

关键词： Complexity theory Visualization Feature extraction Image color analysis Computer vision Noise Correlation

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Target-surround feature attention model of visual tracking

Target-surround feature attention model of visual tracking

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for multimedia, signal and vision processing, CIMSIVP 2011

作者： Huang, Yu-Wei Lin, Wei-Song Lin, Ru-Je Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781424499151

This paper presents a target-surround feature attention (TSFA) model for constructing attention-based visual tracking algorithm. This model extracts attentive region by distinguishing the color contrast between the interested target and its surround. A preference generator provides online feature transformation to update the target/surround biasing masks that describes the color composition associated with the target and its surround. Output of the TSFA model is a saliency map representing occurrence possibility of the target. Tracker based on the mean shift algorithm is used to lock and locate the target on the saliency map. Experimental results show that visual tacking algorithm with the TSFA model may adapt to noisy images under changing illumination. © 2011 ieee.

关键词： Computer vision

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Dynamic vision sensor camera based bare hand gesture recognition

Dynamic vision sensor camera based bare hand gesture recogni...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for multimedia, signal and vision processing, CIMSIVP 2011

作者： Ahn, Eun Yeong Lee, Jun Haeng Mullen, Tracy Yen, John Information Sciences and Technology Pennsylvania State University University Park PA United States Advanced Samsung Institute of Technology Samsung Electronics Yongin-si Gyeonggi-do Korea Republic of

ISBN: (纸本)9781424499151

This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition. © 2011 ieee.

关键词： Cameras

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Investigation of local and global features for face detection

Investigation of local and global features for face detectio...

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symposium Series on computational intelligence, ieee SSCI 2011 - 2011 ieee symposium on computational intelligence for multimedia, signal and vision processing, CIMSIVP 2011

作者： Pereira, Eanes Gomes, Herman Moura, Eduardo Carvalho, João Zhang, Tong Center for Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Paraíiba Brazil Multimedia Interaction and Understanding Lab Hewlett-Packard Laboratories Palo Alto CA United States

ISBN: (纸本)9781424499151

This work is concerned with the empirical evaluation of a set of local and global features under the context of frontal (including semi-profile) and full profile face classification. Integral LBP, Integral Histograms, PCA and Optimized Face Ratios features have been evaluated using SVM classifiers. A data set of about 14,000 face and 300,000 non face images has been used in the experiments. Face images were obtained from well known public face research databases, such as BioID, Color Feret, CMU PIE, among others. The PCA-SVM classifier presented best overall results for both frontal and full profile faces whereas the classifier based on Face Ratios presented the lowest classification rates. A weighted combination of all classifiers yielded high True Positive (TPR) and True Negative (TNR) rates: 91.7% and 100%, respectively, for the frontal face experiments;99.59% and 99.62%, respectively, for the profile face experiments. These results indicate that the evaluated features are very suitable to the problem of face detection and that a simple classifier combination improves individual classifiers performance. © 2011 ieee.

关键词： Face recognition

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Dynamic vision sensor camera based bare hand gesture recognition

Dynamic vision sensor camera based bare hand gesture recogni...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Eun Yeong Ahn Jun Haeng Lee Tracy Mullen John Yen Information Sciences and Technology Pennsylvania State University University Park PA USA Advanced Samsung Institute of Technology Samsung Electronics Company Limited Yongin si Gyeonggi South Korea

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Investigation of local and global features for face detection

Investigation of local and global features for face detectio...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Eanes Pereira Herman Gomes Eduardo Moura João Carvalho Tong Zhang Center of Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Paraiba Brazil Multimedia Interaction and Understanding Laboratory Hewlett Packard Laboratories Palo Alto CA USA

This work is concerned with the empirical evaluation of a set of local and global features under the context of frontal (including semi-profile) and full profile face classification. Integral LBP, Integral Histograms, PCA and Optimized Face Ratios features have been evaluated using SVM classifiers. A data set of about 14,000 face and 300,000 non face images has been used in the experiments. Face images were obtained from well known public face research databases, such as BioID, Color Feret, CMU PIE, among others. The PCA-SVM classifier presented best overall results for both frontal and full profile faces whereas the classifier based on Face Ratios presented the lowest classification rates. A weighted combination of all classifiers yielded high True Positive (TPR) and True Negative (TNR) rates: 91.7% and 100%, respectively, for the frontal face experiments; 99.59% and 99.62%, respectively, for the profile face experiments. These results indicate that the evaluated features are very suitable to the problem of face detection and that a simple classifier combination improves individual classifiers performance.

关键词： Face Feature extraction Histograms Support vector machines Genetic algorithms Principal component analysis Face detection

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What makes motion meaningful? Affective properties of abstract motion

What makes motion meaningful? Affective properties of abstra...

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Pacific Rim symposium on Image and Video Technology

作者： Bartram, Lyn Nakatani, Ai School of Interactive Arts and Technology Simon Fraser University Surrey BC Canada

ISBN: (纸本)9780769542850

Motion provides a rich modality for communicating emotion and creating affect, and developing techniques for analyzing, manipulating and enhancing animations in video and computer graphics is an active research area. However, we have little empirical evidence of how particular attributes of motion elicit particular impressions. In this paper we describe a study of how perceptual attributes of abstract motion contribute to affective interpretation. Our results show that even small simple motions can reliably evoke certain impressions given particular motion attributes. These results contribute to our understanding of computational aesthetics and have implications for the design of media experiences, visualization, interactive art and immersive environments. They also provide insight into what properties of motion might prove most evocative in video and animation textures. © 2010 ieee.

关键词： 4.V.I. Information interfaces H.1.2 User/Machine System abstract motion animation enhancement animation manipulation animation textures art computational aesthetics computer animation computer graphics image enhancement image motion analysis image texture interactive art interactive systems multimedia information systems presentation video signal processing video textures

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vision-Inertial Tracking System for Robust Fiducials Registration in Augmented Reality

Vision-Inertial Tracking System for Robust Fiducials Registr...

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ieee symposium on computational intelligence for multimedia signal and vision processing

作者： Maidi, Madjid Ababsa, Fakhreddine Mallem, Malik Univ Evry Val Essonne Comp Sci & Complex Syst Lab IBISC F-91020 Evry France

ISBN: (纸本)9781424427710

This paper describes a multimodal tracking system to resolve occlusions in augmented reality applications. The first module of the proposed architecture is composed of a vision based system and allows identification and tracking of visible targets. When targets are partially occluded by scene elements, a second module relieves the vision based module and tracks feature points using a robust algorithm. Finally, a multi-sensors tracking approach is implemented to handle total occlusion of targets and maintains registration even if all markers are not visible. Experimental results and many evaluations have been performed to show the efficiency and robustness of the proposed multimodal approach of tracking and occlusion handling in augmented reality.

关键词： Augmented reality

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vision-inertial tracking system for robust fiducials registration in augmented reality

Vision-inertial tracking system for robust fiducials registr...

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ieee symposium on computational intelligence for multimedia signal and vision processing, cimsvp

作者： Madjid Maidi Fakhreddine Ababsa Malik Mallem Computer Science and Complex Systems Laboratory (IBISC) University of Evry Val d''Essonne (UEVE) Evry France

关键词： Robustness Augmented reality Target tracking Cameras Sensor systems Machine vision Layout Computer vision Predictive models Performance evaluation

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