NDLI: An experimental comparison of clustering methods for content-based indexing of large image databases

Content Provider	Springer Nature Link
Author	Lai, Hien Phuong Visani, Muriel Boucher, Alain Ogier, Jean Marc
Copyright Year	2012
Abstract	In recent years, the expansion of acquisition devices such as digital cameras, the development of storage and transmission techniques of multimedia documents and the development of tablet computers facilitate the development of many large image databases as well as the interactions with the users. This increases the need for efficient and robust methods for finding information in these huge masses of data, including feature extraction methods and feature space structuring methods. The feature extraction methods aim to extract, for each image, one or more visual signatures representing the content of this image. The feature space structuring methods organize indexed images in order to facilitate, accelerate and improve the results of further retrieval. Clustering is one kind of feature space structuring methods. There are different types of clustering such as hierarchical clustering, density-based clustering, grid-based clustering, etc. In an interactive context where the user may modify the automatic clustering results, incrementality and hierarchical structuring are properties growing in interest for the clustering algorithms. In this article, we propose an experimental comparison of different clustering methods for structuring large image databases, using a rigorous experimental protocol. We use different image databases of increasing sizes (Wang, PascalVoc2006, Caltech101, Corel30k) to study the scalability of the different approaches.
Starting Page	345
Ending Page	366
Page Count	22
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	15
Issue Number	4
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-01-13
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image indexing Feature space structuring Clustering Large image database Content-based image retrieval Unsupervised classification Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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