Constructing visual phrases for effective and efficient object-based image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Shape Based Detection and Top-Down Delineation Using Image Segments
International Journal of Computer Vision
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semantics extraction from images
Knowledge-driven multimedia information extraction and ontology evolution
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We introduce a method for object class detection and localization which combines regions generated by image segmentation with local patches. Region-based descriptors can model and match regular textures reliably, but fail on parts of the object which are textureless. They also cannot repeatably identify interest points on their boundaries. By incorporating information from patch-based descriptors near the regions into a new feature, the Region-based Context Feature (RCF), we can address these issues. We apply Region-based Context Features in a semi-supervised learning framework for object detection and localization. This framework produces object-background segmentation masks of deformable objects. Numerical results are presented for pixel-level performance.