Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
International Journal of Computer Vision
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theory of keyblock-based image retrieval
ACM Transactions on Information Systems (TOIS)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visually Searching the Web for Content
IEEE MultiMedia
IEEE Transactions on Image Processing
Image classification for content-based indexing
IEEE Transactions on Image Processing
VisMed: a visual vocabulary approach for medical image indexing and retrieval
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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Consumer photos exhibit highly varied contents, diverse resolutions and inconsistent quality. The objects are usually ill-posed, occluded, and cluttered with poor lighting, focus, and exposure. Existing image retrieval approaches face many obstacles such as robust object segmentation, small sampling problem during relevance feedback, semantic gap between low-level features and high-level semantics, etc. We propose a structured learning approach to design domain-relevant visual semantics, known as semantic support regions, to support semantic indexing and visual query for consumer photos. Semantic support regions are segmentation-free image regions that exhibit semantic meanings and that can be learned statistically to span a new indexing space. They are detected from image content, reconciled across multiple resolutions, and aggregated spatially to form local semantic histograms. Query by Spatial Icons (QBSI) is a unique visual query language to specify semantic icons and spatial extents in a Boolean expression. Based on 2400 heterogeneous consumer photos and 26 semantic support regions learned from a small training set, we demonstrate the usefulness of the visual query language with 15 QBSI queries that have attained high precision values at top retrieved images.