Towards Data-Adaptive and User-Adaptive Image Retrieval by Peer Indexing

Adaptation to the characteristics of specific images and the preferences of individual users is critical to the success of an image retrieval system but insufficiently addressed by the existing approaches. In this paper, we propose an elegant and effective approach to data-adaptive and user-adaptive image retrieval based on the idea of peer indexing—describing an image through semantically relevant peer images. Specifically, we associate each image with a two-level peer index that models the “data characteristics” of the image as well as the “user characteristics” of individual users with respect to this image. Based on two-level image peer indexes, a set of retrieval parameters including query vectors and similarity metric are optimized towards both data and user characteristics by applying the pseudo feedback strategy. A cooperative framework is proposed under which peer indexes and image visual features are integrated to facilitate data- and user-adaptive image retrieval. Simulation experiments conducted on real-world images have verified the effectiveness of our approach in a relatively restricted setting.

Authors and Affiliations

1. Language Technology Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

   Jun Yang

2. Department of Computer Engineering and Information Technology, City, University of Hong Kong, 83 Tat Chee Avenue, Kowloon, HKSAR, China

   Qing Li

3. Department of Computer Science, Zhejiang University, Hangzhou, China, 310027

   Yueting Zhuang

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