Exploiting the JPEG Compression Scheme for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compressed-domain techniques for image/video indexing and manipulation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
DCT histogram optimization for image database retrieval
Pattern Recognition Letters
Image retrieval based on energy histograms of the low frequency DCT coefficients
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Fast scene change detection using direct feature extraction fromMPEG compressed videos
IEEE Transactions on Multimedia
Spatial color descriptor for image retrieval and video segmentation
IEEE Transactions on Multimedia
A compressed domain scheme for classifying block edge patterns
IEEE Transactions on Image Processing
DCT Sign-Based Similarity Measure for JPEG Image Retrieval
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A Decision Tree Approach for Scene Pattern Recognition and Extraction in Snooker Videos
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Indexing and retrieval of visually similar images in the orthogonal polynomials transform domain
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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A new algorithm for compressed image retrieval is proposed in this paper based on DCT block edge patterns. This algorithm directly extract three edge patterns from compressed image data to construct an edge pattern histogram as an indexing key to retrieve images based on their content features. Three feature-based indexing keys are described, which include: (i) the first two features are represented by 3-D and 4-D histograms respectively; and (ii) the third feature is constructed by following the spirit of run-length coding, which is performed on consecutive horizontal and vertical edges. To test and evaluate the proposed algorithms, we carried out two-stage experiments. The results show that our proposed methods are robust to color changes and varied noise. In comparison with existing representative techniques, the proposed algorithms achieves superior performances in terms of retrieval precision and processing speed.