Abstract
Relevance feedback has been shown as a powerful tool to improve the retrieval performance of content-based image retrieval (CBIR). However, the feedback iteration process is tedious and time-consuming. History log consists of valuable information about previous users’ perception of the content of image and such information can be used to accelerate the feedback iteration process and enhance the retrieval performance. In this paper, a novel algorithm to collect and compute the log-based relevance of the images is proposed. We utilize the multi-level structure of log-based relevance and fully mine previous users’ perception of content of images in log. Experimental results show that our algorithm is effective and outperforms previous schemes.
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Zhang, H., Sun, W., Dong, S., Chen, L., Lin, C. (2010). Multi-level Log-Based Relevance Feedback Scheme for Image Retrieval. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_56
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DOI: https://doi.org/10.1007/978-3-642-17313-4_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17312-7
Online ISBN: 978-3-642-17313-4
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