Title :
Automatic Query Type Classification for Web Image Retrieval
Author :
Cai, Keke ; Bu, Jiajun ; Chen, Chun ; Huang, Peng
Author_Institution :
Zhejiang Univ., Hangzhou
Abstract :
In this paper, a framework of query classification is proposed for text-based image retrieval. The classification process in this framework consists of three phases. In the first phase, two basic classifiers are employed to classify the image query into certain categories. They are easily implemented but have weak adaptability to various web image queries. Therefore, in the second phase, an expanded classifier is implemented to compensate for the possible incapability of the basic classifiers. In this step, the inferential relationships extracted by information flow (IF) are exploited to disclose the meaning underneath the image query, which makes the classification more accurate. In the third phase, the strengths of all three individual classifiers are leveraged together to achieve the most possible efficient classification. The experimental results prove the effectiveness of this framework in dealing with image query classification.
Keywords :
Internet; image classification; image retrieval; text analysis; Web image queries; Web image retrieval; automatic query type classification; basic classifiers; expanded classifier; inferential relationships; information flow; text-based image retrieval; Computational efficiency; Computer science; Data mining; Educational institutions; Image analysis; Image classification; Image retrieval; Internet; Learning systems; Search engines;
Conference_Titel :
Multimedia and Ubiquitous Engineering, 2007. MUE '07. International Conference on
Conference_Location :
Seoul
Print_ISBN :
0-7695-2777-9
DOI :
10.1109/MUE.2007.96