Indexing and integrating multiple features for WWW images

Shen, Heng Tao, Zhou, Xiaofang and Cui, Bin (2006) Indexing and integrating multiple features for WWW images. World Wide Web: Internet and Web Information Systems, 9 3: 343-364. doi:10.1007/s11280-006-8560-4

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Author Shen, Heng Tao
Zhou, Xiaofang
Cui, Bin
Title Indexing and integrating multiple features for WWW images
Journal name World Wide Web: Internet and Web Information Systems   Check publisher's open access policy
ISSN 1386-145X
1573-1413
Publication date 2006
Sub-type Article (original research)
DOI 10.1007/s11280-006-8560-4
Volume 9
Issue 3
Start page 343
End page 364
Total pages 22
Editor M. Rusinkiewicz
Y. Zhang
Place of publication Secaucus, NJ, United States
Publisher Springer
Collection year 2006
Language eng
Subject C1
280103 Information Storage, Retrieval and Management
700103 Information processing services
Abstract In this paper, we present a novel indexing technique called Multi-scale Similarity Indexing (MSI) to index image's multi-features into a single one-dimensional structure. Both for text and visual feature spaces, the similarity between a point and a local partition's center in individual space is used as the indexing key, where similarity values in different features are distinguished by different scale. Then a single indexing tree can be built on these keys. Based on the property that relevant images have similar similarity values from the center of the same local partition in any feature space, certain number of irrelevant images can be fast pruned based on the triangle inequity on indexing keys. To remove the dimensionality curse existing in high dimensional structure, we propose a new technique called Local Bit Stream (LBS). LBS transforms image's text and visual feature representations into simple, uniform and effective bit stream (BS) representations based on local partition's center. Such BS representations are small in size and fast for comparison since only bit operation are involved. By comparing common bits existing in two BSs, most of irrelevant images can be immediately filtered. To effectively integrate multi-features, we also investigated the following evidence combination techniques-Certainty Factor, Dempster Shafer Theory, Compound Probability, and Linear Combination. Our extensive experiment showed that single one-dimensional index on multi-features improves multi-indices on multi-features greatly. Our LBS method outperforms sequential scan on high dimensional space by an order of magnitude. And Certainty Factor and Dempster Shafer Theory perform best in combining multiple similarities from corresponding multiple features.
Keyword Indexing
Clustering
Www
Multi-features
Image Retrieval
Computer Science, Information Systems
Computer Science, Software Engineering
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Wed, 15 Aug 2007, 08:49:59 EST