Content-Based Image Retrieval Using Iterative Search

The aim of Content-based Image Retrieval (CBIR) is to find a set of images that best match the query based on visual features. Most existing CBIR systems find similar images in low level features, while Text-based Image Retrieval (TBIR) systems find images with relevant tags regardless of contents in the images. Generally, people are more interested in images with similarity both in contours and high-level concepts. Therefore, we propose a new strategy called Iterative Search to meet this requirement. It mines knowledge from the similar images of original queries, in order to compensate for the missing information in feature extraction process. To evaluate the performance of Iterative Search approach, we apply this method to four different CBIR systems (HOF Zhou et al. in ACM international conference on multimedia, 2012; Zhou and Zhang in Neural information processing—international conference, ICONIP 2011, Shanghai, 2011, HOG Dalal and Triggs in IEEE computer society conference on computer vision pattern recognition, 2005, GIST Oliva and Torralba in Int J Comput Vision 42:145–175, 2001 and CNN Krizhevsky et al. in Adv Neural Inf Process Syst 25:2012, 2012) in our experiments. The results show that Iterative Search improves the performance of original CBIR features by about 20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document} on both the Oxford Buildings dataset and the Object Sketches dataset. Meanwhile, it is not restricted to any particular visual features.