Paper Info
Title
Relevance feedback for semantics based image retrieval
Abstract
Content based image retrieval is one of the most active research areas in the field of multimedia technology. Currently, the relevance feedback approach has attracted great attention since it can bridge the gap between low-level features and the semantics of images. We propose a new relevance feedback technique, which uses the normal mixture model for the high-level similarity metric of the user's intention and estimates the unknown parameters from the user's feedback. Our approach is based on a novel hybrid algorithm where the criterion for the selection of the display image set is evolved from the most informative to the most probable as the retrieval process progresses. Experiments on the Corel image set show that the proposed algorithm outperforms MindReader at the semantics based search
Year
DOI
Venue
2001
10.1109/ICIP.2001.958948
ICIP (1)
Keywords
Field
DocType
corel image set,mindreader,image processing,image semantics,display image set,visual databases,high-level similarity metric,semantics based image retrieval,hybrid algorithm,low level features,image retrieval,content based image retrieval,relevance feedback,semantics based search,multimedia technology,mixture model,multimedia communication,content-based retrieval,em algorithm,space technology,shape,information technology,feedback,displays,parameter estimation
Computer vision,Automatic image annotation,Relevance feedback,Human–computer information retrieval,Information retrieval,Computer science,Image retrieval,Image processing,Artificial intelligence,Mixture model,Content-based image retrieval,Visual Word
Conference
Volume
ISSN
ISBN
1
1522-4880
0-7803-6725-1
Citations 
PageRank 
References 
4
0.79
3
Authors
2
Name
Order
Citations
PageRank
Janghyun Yoon141.46
Nikil Jayant210223.17