By Topic

From Document to Image: Learning a Scalable Ranking Model for Content Based Image Retrieval

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Chao Zhou ; Key Lab. of Machine Perception, Peking Univ., Beijing, China ; Yangxi Li ; Bo Geng ; Chao Xu

With the recent advancement of web search ranking framework, a.k.a. learning to rank, it is questionable whether it can be still applicable to the large-scale content based image retrieval settings. Moreover, given the complex structure of image representation, it is also challenging how to design visual ranking features that not only scale up well, but also model various visual modalities and the spatial distributions of local features. In this paper, we answer the above two questions by investigating the performance of learning to rank for the large-scale content based image retrieval problem, with some scalable visual based ranking features proposed to improve the performance. Specifically, we firstly adopt several well performed ad-hoc ranking models to generate the Bag-of-Visual-Words based ranking features. Additionally, to preserve the spatial information of image local descriptors, we split images into blocks from coarse to fine, and extract ranking features hierarchically with a spatial pyramid manner. Finally, image global features are also quantized via LSH and concatenated with the existing ranking features all together. Experimental results on both Oxford and Image Net databases demonstrate the effectiveness and efficiency of the proposed ranking model, as well as the complementarity of each ranking features.

Published in:

Multimedia and Expo Workshops (ICMEW), 2012 IEEE International Conference on

Date of Conference:

9-13 July 2012