Slide: Saliency guided image dictionary and image similarity evaluation
In this paper we present a novel idea of evaluating similarity between two images aided by a salient object detection framework. For computing similarity between images consisting of multiple objects and varying background, extracting features relevant to the object of interest is of cruicial importance. To accomplish this task, we employ a saliency guided dictionary learning framework for image similarity evaluation (SLIDE). The saliency detection framework emphasizes the image regions that attract human attention and is exploited to build a dictionary for generating sparse representation of the images. The compressibility of the sparse codes is exploited in computing the similarity measure. The SLIDE framework is used in the application of image retrieval for three military image datasets and shows in average an improvement of 14.8% in retrieval accuracy compared to two state-of-the-art similarity measures - information distance and sparse representation based compression distance.