Paper Info
Title
Automatic Query Type Classification for Web Image Retrieval
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.
Year
DOI
Venue
2007
10.1109/MUE.2007.96
MUE
Keywords
Field
DocType
web image,classification process,basic classifier,web image queries,information flow,expanded classifier,web image retrieval,image query,basic classifiers,inferential relationships,image query classification,text-based image retrieval,automatic query type classification,image classification,possible incapability,image retrieval,internet,various web image query,possible efficient classification,text analysis,query classification,search engines,data mining,computer science,image analysis
Query optimization,Data mining,Query language,Search engine,Query expansion,Pattern recognition,Computer science,Web query classification,Image retrieval,Artificial intelligence,Classifier (linguistics),Contextual image classification
Conference
ISBN
Citations 
PageRank 
0-7695-2777-9
2
0.37
References 
Authors
8
4
Name
Order
Citations
PageRank
Keke Cai124315.36
Jiajun Bu24106211.52
Chun Chen34727246.28
Peng Huang4964.92