DocumentCode :
2504976
Title :
Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition
Author :
Han, Xian-Hua ; Chen, Yen-wei ; Ruan, Xiang
Author_Institution :
Ritsumeikan Univ., Kusatsu, Japan
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
3045
Lastpage :
3048
Abstract :
In this paper, we present a novel semi-supervised and interactive concept learning algorithm for scene recognition by local semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to model the semantic content of images. The basic idea of the semantic modeling is to classify local image regions into semantic concept classes such as water, sunset, or sky. However, labeling concept sampling manually for training semantic model is fairly expensive, and the labeling results is, to some extent, subjective to the operators. In this paper, by using the proposed semi-supervised and interactive learning algorithm, training samples and new concepts can be obtained accurately and efficiently. Through extensive experiments, we demonstrate that the image concept representation is well suited for modeling the semantic content of heterogenous scene categories, and thus for recognition and retrieval. Furthermore, higher recognition accuracy can be achieved by updating new training samples and concepts, which are obtained by the novel proposed algorithm.
Keywords :
content-based retrieval; image classification; image representation; image retrieval; learning (artificial intelligence); concept sampling labeling; content-based image retrieval; image concept representation; image retrieval; interactive semantic concept learning; local image region classification; local semantic description; scene recognition; semantic concept class; semantic model training; semantic modeling; semisupervised learning; Fires; Image recognition; Image representation; Kernel; Semantics; Snow; Training; Semi-supervised learning; iteractive; scene recognition; semantic model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.746
Filename :
5597301
Link To Document :
بازگشت