Sparsity cue in image copy detection

Authors:
Huan-Cheng Hsu;Chun-Rong Huang;Chun-Shien Lu
Affiliations:
National Chung Hsing University, Taichung, Taiwan Roc;National Chung Hsing University, Taichung, Taiwan Roc;Academia Sinica, Taipei, Taiwan Roc
Venue:
Proceedings of the 20th ACM international conference on Multimedia
Year:
2012

Citing 8
Cited 0

Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
An efficient parts-based near-duplicate and sub-image retrieval system

Proceedings of the 12th annual ACM international conference on Multimedia
Contrast context histogram-An efficient discriminating local descriptor for object recognition and image matching

Pattern Recognition
Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection

IEEE Transactions on Image Processing
Online Learning for Matrix Factorization and Sparse Coding

The Journal of Machine Learning Research
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

IEEE Transactions on Signal Processing
A New Approach to Image Copy Detection Based on Extended Feature Sets

IEEE Transactions on Image Processing
Feature-Based Sparse Representation for Image Similarity Assessment

IEEE Transactions on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image copy detection is an art of searching duplicates from a target database. Computationally efficient and robust detection is still a challenging issue. Inspired by the recent study of sparsity in the context of compressed sensing, we propose a sparse representation-based image copy detection method exploiting sparsity as the cue for searching duplicates. We find that although sparse representation can describe an image in a compact manner, the inherent discriminable features, as far as we know, are not entirely explored. In this paper, we study the discrimination ability inherent in sparsity via online dictionary learning and compact feature descriptor representation. Experimental results show that our method, compared with state-of-the-art, is computationally efficient and attains better or comparable detection performance measured in terms of precision and recall rates.