Contextual Hashing for Large-Scale Image Search

With the explosive growth of the multimedia data on the Web, content-based image search has attracted considerable attentions in the multimedia and the computer vision community. The most popular approach is based on the bag-of-visual-words model with invariant local features. Since the spatial cont...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 23; no. 4; pp. 1606 - 1614
Main Authors: Liu, Zhen, Li, Houqiang, Zhou, Wengang, Zhao, Ruizhen, Tian, Qi
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.04.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Binary codes
BoVW
Coding, codes
Computer science; control theory; systems
Context
Exact sciences and technology
Feature extraction
geometric verification
Hamming distance
hashing
Image processing
Image search
Indexing
Information theory
Information, signal and communications theory
Invariants
Multimedia
Paris
Pattern recognition. Digital image processing. Computational geometry
Position (location)
Searching
Signal and communications theory
Signal processing
spatial context modeling
Telecommunications and information theory
Vectors
Visualization
World Wide Web
France
Europe
Paris
Ile-de-France
Performance evaluation
Multimedia
Computer vision
Content based retrieval
Image retrieval
Information retrieval
Multimedia communication
Algorithm
Binary code
Computational complexity
Modeling
spatial context modeling
Image search
BoVW
World wide web
Hashing
Localization
geometric verification
Content analysis
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Description
Summary:With the explosive growth of the multimedia data on the Web, content-based image search has attracted considerable attentions in the multimedia and the computer vision community. The most popular approach is based on the bag-of-visual-words model with invariant local features. Since the spatial context information among local features is critical for visual content identification, many methods exploit the geometric clues of local features, including the location, the scale, and the orientation, for explicitly post-geometric verification. However, usually only a few initially top-ranked results are geometrically verified, considering the high computational cost in full geometric verification. In this paper, we propose to represent the spatial context of local features into binary codes, and implicitly achieve geometric verification by efficient comparison of the binary codes. Besides, we explore the multimode property of local features to further boost the retrieval performance. Experiments on holidays, Paris, and Oxford building benchmark data sets demonstrate the effectiveness of the proposed algorithm.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2014.2305072