Block-based pseudo-relevance feedback for image retrieval

ABSTRACT

Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback. PRF is used to avoid the limitations of the traditional RF approach, which is a human-in-the-loop process. Although the pseudo-relevance feedback set contains noise, PRF can perform retrieval reasonably effectively. For implementing PRF, the Rocchio algorithm has been considered reasonably effective and is a well-established baseline method. However, it simply treats all of the top k feedback images as being equally similar to the query. Therefore, we present a block-based PRF approach for improving image retrieval performance. In this approach, images in the positive and negative feedback sets are further divided into predefined blocks, each of which contains one to several images, and blocks containing higher- or lower-ranked images will be assigned higher or lower weights, respectively. Experiments using the NUS-WIDE-LITE and Caltech 256 datasets and two different feature representations consistently show that the proposed approach using 30 blocks outperforms the baseline PRF in terms of P@10, P@20, and P@50. Furthermore, we show that a system that incorporates the user’s feedback allows the 30-block-based PRF approach to perform even better.

KEYWORDS:

• Image retrieval
• relevance feedback
• pseudo-relevance feedback
• Rocchio algorithm

Acknowledgments

The research of the corresponding author was supported in part by the Ministry of Science and Technology of Taiwan under Grant MOST 109-2410-H-182-012, in part by the Healthy Aging Research Center, Chang Gung University from the Featured Areas Research Center Program within the Framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan under Grant EMRPD1K0481, EMRPD1K0461 and in part by Chang Gung Memorial Hospital, Linkou under Grant CMRPD3I0031.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. http://www.vision.caltech.edu/Image_Datasets/Caltech256/

Additional information

Funding

This work was supported by the Ministry of Science and Technology of Taiwan [MOST 109-2410-H-182-012 -], and by Chang Gung Memorial Hospital, Linkou [BMRPH13].