Adaptive image segmentation for region-based object retrieval using generalized Hough transform

This page uses JavaScript to progressively load the article content as a user scrolls. Screen reader users, click the load entire article button to bypass dynamically loaded article content. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. This page uses JavaScript to progressively load the article content as a user scrolls. Click the View full text link to bypass dynamically loaded article content. Finding an object inside a target image by querying multimedia data is desirable, but remains a challenge. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of region-based approaches is that perform detection using low level visual features within the region and the homogeneous image regions have little correspondence to the semantic objects. Thus, the retrieval results are often far from satisfactory. In addition, the performance is significantly affected by consistency in the segmented regions of the target object from the query and database images. Instead of solving these problems independently, this paper proposes region-based object retrieval using the generalized Hough transform (GHT) and adaptive image segmentation. The proposed approach has two phases. First, a learning phase identifies and stores stable parameters for segmenting each database image. In the retrieval phase, the adaptive image segmentation process is also performed to segment a query image into regions for retrieving visual objects inside database images through the GHT with a modified voting scheme to locate the target visual object under a certain affine transformation. The learned parameters make the segmentation results of query and database images more stable and consistent. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy, robustness, and execution speed. —CHI-HAN CHUANG received the B.S. degree from Chung Hua University, Hsinchu, Taiwan, in 2002, and M.S. degree in National Taiwan Ocean University, Keelung, Taiwan, in 2007, where he is currently pursuing his Ph.D. degree. His research interests are in image retrieval, computer vision, and intelligent information processing. —SHYI-CHYI CHENG received the B.S. degree from National Tsing Hua University, Hsinchu, Taiwan, R.O.C., in 1986, and the M.S. and Ph.D. degrees in Electronics Engineering and Computer Science and Information Engineering from National ChiaoTung University, Hsinchu, in 1988 and 1992, respectively. From 1992 to 1998, he was a Research Staff Member at Chunghwa Telecom Laboratories, Taoyuan, Taiwan. He is currently a Professor and Chairman of computer science and engineering, National Taiwan Ocean University, Keelung, Taiwan. His research interests include multimedia databases, image/video compression and communications, and artificial neural network applications. —CHIN-CHUN CHANG received the B.S., M.S., and Ph.D. degrees in computer science from National Chiao Tung University, Hsinchu, Taiwan, R.O.C., in 1989, 1991, and 2000, respectively. From 2001 to 2002, he was on the faculty at the Department of Computer Science and Engineering, Tatung University, Taipei, Taiwan. In 2002, he joined the Department of Computer Science, National Taiwan Ocean University, Keelung, where he is currently an Assistant Professor. His research interests include computer vision, machine learning, and pattern recognition. Copyright © 2016 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V. Source.


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