A novel and adaptive method for image search reranking

Abstract

In this paper, we propose a novel and adaptive method for image search reranking. We firstly evaluate different visual features based on the results of image classification on object and scene separately. And visual features are chosen adaptively to rerank the initial image search result. For a given query, it can be classified into either object or scene using the trained classifier on text features. Then, low-level visual features are adaptively selected and fused for image search reranking. Experimental results on large scale image dataset of WebQueries demonstrate the efficacy of the proposed method. © 2012 Springer-Verlag Berlin Heidelberg.