Weight modulation in top–down computational model for target search

Abstract

Computer vision research aims at building models which mimic human systems. The recent development in visual information have been used to derive computational models which address a variety of applications. Biological models help to identify the salient objects in the image. But, the identification of non-salient objects in a heterogeneous environment is a challenging task that requires a better understanding of the visual system. In this work, a weight modulation based top-down model is proposed that integrates the visual features that depend on its importance for the target search application. The model is designed to learn the optimal weights such that it biases the features of the target from the other surrounding regions. Experimental analysis is performed on various scenes on a standard dataset with the selected object in the scene. Metrics such as area under curve, average hit number and correlation reveal that the method is more suitable in target identification, by suppressing the other region.