Leveraging Exemplar and Saliency Model for Image Search Reranking

Abstract

In this paper, we propose to rerank the image retrieval results using a novel method which can be fitted to both objects classes and scenes classes. We first introduce the two methods: Exemplar model and Saliency Map (SM). Exemplar model is a top-down method which considers region of interest (ROI) of images from the same class containing lots of similar discriminative local features. These discriminative local features can be trained as the model of the specific class and to rerank the retrieved images by their similarities with the trained model of the query class. On the other hand, SM is a bottom-up method which uses winner-take-all and inhibition-of-return mechanisms to draw different locations in descending saliency order, and the images can be reranked by their salient scores. In experimental results, we observe that Exemplar Model performs well in object classes and SM performs well in scene classes for these two methods focus on different aspects to rerank images. Then we propose a method named ExSM which combines the advantage of Exemplar model and SM. ExSM inherits the superiority of Exemplar model in object classes and SM in scene classes and outperforms both of them in general.