Cognitive inspired WOR framework to reveal image semantics, for efficient Content Based Image Retrieval

Content Based Image Retrieval (CBIR) involves the process of searching and retrieving relevant images from a database. Most CBIR systems rely on the global content of the image but the desired content in an image is often localized, demanding the need for an object-centric CBIR. We propose a cognitive inspired framework WOR (What Object to Retrieve), to solve the problem of an object-centric CBIR. The key contributions in the proposed approach are: (i) Automatic creation of class dendogram in kernel feature space, for an efficient object recognition task; (ii) Integration of information from "What" and "Where" channels in an iterative feedback mechanism, to filter erroneous contents in the outputs of individual modules. Finally, our system extracts HOG feature descriptor from the output of Where channel, for detecting similarity and rank-order the retrieved samples. Experimentations are done with real-life datasets (including PASCAL VOC) exhibits the superior performance.