A General and Effective Two-Stage Approach for Region-Based Image Retrieval

• Main Authors: Mann-Jung Hsiao, 蕭曼蓉
• Other Authors: Yo-Ping Huang
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/50412986349196195974

博士 === 大同大學 === 資訊工程學系(所) === 99 === With the rapid growth of multimedia applications and digital archives, content-based image retrieval (CBIR) has received lots of attentions and emerged as an important research area for the past decades. CBIR tends to automatically index and retrieve images based on their low-level contents, which is a complex and challenging problem spanning diverse algorithms all over the retrieval processes including color space selection, feature extraction, similarity measurement, retrieval strategies, relevance feedback, etc. In these issues, “semantic gap” is still an open challenging problem in CBIR. It reflects the discrepancy between low-level features developed by the retrieval algorithm and high-level concepts required by users. Some research works attempt to narrow this gap by utilizing regional features, which are known as region-based image retrieval (RBIR). RBIR tends to search the interesting regions that are closed to the query target, instead of the whole images. It contributes to more meaningful image retrieval; however, the image segmentation algorithms are complex and computation intensive and the segmentation results are often not correct. To tackle this problem, we propose a two-stage retrieval strategy to improve the performance of RBIR. At the first stage of retrieval, the threshold-based pruning (TBP) serves as a filter to quickly remove those candidates with widely distinct global features. At the second stage, a more detailed feature comparison (DFC) method is conducted over the remaining candidates, focusing on the region of interest (ROI). In the experimental system, users can choose their ROI in the query image and interact with the system by selecting different strategies, setting parameter values, and adjusting the weights of features as the search progresses. The experimental results show that both efficiency and accuracy can be respectively improved by 10.7% and 7.1% using the proposed two-stage approach.