Image parsing by loopy dynamic programming

作     者：Zhang, Shizhou Wang, Jinjun Gong, Yihong Zhang, Shun Zhang, Xinzi Lan, Xuguang 

作者机构：Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China 

出 版 物：《NEUROCOMPUTING》 (神经计算)

年 卷 期：2014年第145卷

页      面：240-249页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Basic Research Program of China (973 Program) [2012CB316400] 

主　　题：Loopy dynamic programming Image parsing Segmentation Detection Classification 

摘      要：The image parsing process gives labels to image regions, as well as information including shape, semantics and context. Although it is one of the most important features in the human visual system, automatic image parsing using computer vision techniques remains difficult due to computational issue. In this paper we introduce a novel method to address this limitation. Our system models an image as a set of regions and uses a novel hypotheses generation algorithm to get possible image parsing solutions for final re-scoring. Our proposed hypotheses generation algorithm, called Loopy Dynamic Programming (LDP), handles large search space efficiently and gives good parsing hypotheses for testing. With such capacity, we are able to apply more precise and complex image models to achieve better performance. In addition, our system can perform image segmentation, detection and classification simultaneously. Experimental results using Pascal VOC 2007 dataset show that the proposed technique achieves very promising performance in all the three tasks. (C) 2014 Elsevier B.V. All rights reserved.