SLAR (Simultaneous Localization and Recognition) framework for smart CBIR

Abstract

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. © 2012 Springer-Verlag.