Image categorization using a semantic hierarchy model with sparse set of salient regions  

作　　者：Chunping LIU Yang ZHENG Shengrong GONG 

机构地区：[1]School of Computer Science and Technology, Soochow University, Suzhou 215006, China [2]The Second Hospital of Nanjing, Nanjing 210003, China

出　　处：《Frontiers of Computer Science》2013年第6期838-851,共14页中国计算机科学前沿（英文版）

基　　金：Acknowledgements This work was supported by National Natural Science Foundation of China （Grant Nos. 61272258, 61170124, 61170020, 61070223）, and Application Foundation Research Plan of Suzhou City, China （SYG201116）.

摘　　要：Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree （HSLT） model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton＇s semantic texton forests （STFs） method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimefital results showed that the use of semantic hierarchies as a hierarchical organizing frame- work provides a better image annotation and organization, improves the accuracy and reduces human＇s effort.Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree （HSLT） model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton＇s semantic texton forests （STFs） method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimefital results showed that the use of semantic hierarchies as a hierarchical organizing frame- work provides a better image annotation and organization, improves the accuracy and reduces human＇s effort.

关 键 词：salient region sparse set semantic hierarchy image annotation image categorization 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP393[自动化与计算机技术—计算机科学与技术]