Scale-Rotation Invariant Pattern Entropy for Keypoint-Based Near-Duplicate Detection

Near-duplicate (ND) detection appears as a timely issue recently, being regarded as a powerful tool for various emerging applications. In the Web 2.0 environment particularly, the identification of near-duplicates enables the tasks such as copyright enforcement, news topic tracking, image and video...

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Bibliographic Details
Published in:IEEE transactions on image processing 2009-02, Vol.18 (2), p.412-423
Main Authors: ZHAO, Wan-Lei, NGO, Chong-Wah
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Entropy
Exact sciences and technology
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image retrieval
Information theory
Information, signal and communications theory
Invariants
Keypoints
Large-scale systems
Matching
near-duplicate detection
Neodymium
Object detection
pattern entropy (PE)
Pattern matching
Pattern Recognition, Automated - methods
Photometry
Quantization
Reproducibility of Results
Rotation
Searching
Sensitivity and Specificity
Signal processing
Strontium
Subtraction Technique
Telecommunications and information theory
Video Recording - methods
visual vocabulary
Vocabulary
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Summary:Near-duplicate (ND) detection appears as a timely issue recently, being regarded as a powerful tool for various emerging applications. In the Web 2.0 environment particularly, the identification of near-duplicates enables the tasks such as copyright enforcement, news topic tracking, image and video search. In this paper, we describe an algorithm, namely Scale-Rotation invariant Pattern Entropy (SR-PE), for the detection of near-duplicates in large-scale video corpus. SR-PE is a novel pattern evaluation technique capable of measuring the spatial regularity of matching patterns formed by local keypoints. More importantly, the coherency of patterns and the perception of visual similarity, under the scenario that there could be multiple ND regions undergone arbitrary transformations, respectively, are carefully addressed through entropy measure. To demonstrate our work in large-scale dataset, a practical framework composed of three components: bag-of-words representation, local keypoint matching and SR-PE evaluation, is also proposed for the rapid detection of near-duplicates.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2008.2008900