Localized Content-based Image Retrieval Using Saliency-based Graph Learning Framework
Localized content-based image retrieval (LCBIR) has emerged as a hot topic more recently due to the fact that in the scenario of CBIR, the user is interested in a portion of the image and the rest of the image is irrelevant. In this paper, we propose a novel region-level relevance feedback method to solve the LCBIR problem. Firstly, the visual attention model is employed to measure the regional saliency of each image in the feedback image set provided by the user. Secondly, the regions in the image set are constructed to form an affinity matrix and a novel propagation energy function is defined which takes both lowlevel visual features and regional significance into consideration. After the iteration, regions in the positive images with high confident scores are selected as the candidate query set to conduct the next-round retrieval task until the retrieval results are satisfactory. Experimental results conducted on both COREL and SIVAL datasets demonstrate the effectiveness of the proposed approach.
localized CBIR graph learning visual attention relevance feedback
Songhe Feng Congyan Lang De Xu
School of Computer & Information Technology, Beijing Jiaotong University, Beijing, China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
1029-1032
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）