Applied multivariate statistical analysis
Applied multivariate statistical analysis
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Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supporting similarity queries in MARS
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
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Semantic based image retrieval: a probabilistic approach
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
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MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Factor graph framework for semantic video indexing
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Attention-driven image interpretation with application to image retrieval
Pattern Recognition
Semantic image classification using statistical local spatial relations model
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Expert Systems with Applications: An International Journal
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Unconstrained consumer photos pose great challenge for content-based image retrieval. Unlike professional images or domain-specific images, consumer photos vary significantly. More often than not, the objects in the photos are ill-posed, occluded, and cluttered with poor lighting, focus and exposure. In this paper, we propose a cascading framework for combining intra-image and inter-class similarities in image retrieval, motivated from probabilistic Bayesian principles. Support vector machines are employed to learn local view-based semantics based on just-in-time fusion of color and texture features. A new detection-driven block-based segmentation algorithm is designed to extract semantic features from images. The detection-based indexes also serve as input for support vector learning of image classifiers to generate class-relative indexes. During image retrieval, both intra-image and inter-class similarities are combined to rank images. Experiments using query-by-example on 2400 genuine heterogeneous consumer photos with 16 semantic queries show that the combined matching approach is better than matching with single index. It also outperformed the method of combining color and texture features by 55% in average precision.