Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
A novel log-based relevance feedback technique in content-based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
RoboGene: an image retrieval system with multi-level log-based relevance feedback scheme
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
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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.