A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
A smart content-based image retrieval system based on color and texture feature
Image and Vision Computing
A unified image retrieval framework on local visual and semantic concept-based feature spaces
Journal of Visual Communication and Image Representation
Image retrieval based on multi-texton histogram
Pattern Recognition
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In traditional content-based image retrieval (CBIR) methods, features are extracted from the entire image for computing similarity with query. It is necessary to design a smart object-centric CBIR to retrieve images from the gallery, having objects similar to that present in the foreground of the query image. We propose a model for a novel SLAR (Simultaneous Localization And Recognition) framework for solving this problem of smart CBIR, to simultaneously: (i) detect the location and (ii) recognize the type (ID or class) of the foreground object in a scene. The framework integrates both unsupervised and supervised methods of foreground segmentation and object classification. This model is motivated by the cognitive models of human visual perception, which generalizes from examples to simultaneously locate and categorize objects. Experimentation has been done on six categories of objects and the results have been compared with a contemporary work on CBIR.