Abstract
A novel approach related to query expansion is proposed to improve image retrieval performance. The proposed approach investigates the problem that not all of the visual features extracted from images are appropriate to be employed for similarity matching. To address this issue, we distinguish image features as effective features from noisy features. The former is benefit for image retrieval while the latter causes deterioration, since the matching of noisy features may rise the similarity score of irrelevant images. In this work, a detailed illustration of effective and noisy features is given and the aforementioned problem is solved by selecting effective features to enhance query feature set while removing noisy features via spatial verification. Experimental results demonstrate that the proposed approach outperforms a number of state-of-the-art query expansion approaches.
This work was supported in part by the National Natural Science Foundation of China under Grant 61472281, the “Shu Guang” project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 12SG23, and the Program for Professor of Special Appointment (Eastern Scholar) at the Shanghai Institutions of Higher Learning under Grant GZ2015005.
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Wang, H., Sun, T. (2016). Improving Image Retrieval by Local Feature Reselection with Query Expansion. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_7
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