DocumentCode :
3120653
Title :
Challenges & approaches in multi-label image annotation
Author :
Kalaivani, A. ; Chitrakal, S.
Author_Institution :
Dept. of Comput. Applic., SRM Group of Instn., Chennai, India
fYear :
2013
fDate :
4-6 July 2013
Firstpage :
1
Lastpage :
8
Abstract :
Multi-label image annotation has received significant attention in the research community over the past few years. Multi-label automatic image annotation assigns keywords to the image based on low level features automatically. In this paper, we present an extensive survey on the research work carried out in the area of multi-label image annotation by using statistical and machine learning approaches for natural images. Multi-label research work carried out in medical and spatial domain are also specified. Finally, paper is concluded towards challenges in multi-label image annotation for future research.
Keywords :
content-based retrieval; image retrieval; learning (artificial intelligence); statistical analysis; content based image retrieval system; machine learning; medical domain; multilabel image annotation; natural images; research community; spatial domain; statistical learning; Artificial neural networks; Classification algorithms; Computational modeling; Feature extraction; Image segmentation; Support vector machines; Training; CBIR; Multi-Label AIA; Semantic Gap;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on
Conference_Location :
Tiruchengode
Print_ISBN :
978-1-4799-3925-1
Type :
conf
DOI :
10.1109/ICCCNT.2013.6726482
Filename :
6726482
Link To Document :
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