Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Unified feature analysis in JPEG and JPEG 2000-compressed domains
Pattern Recognition
Texture features for DCT-coded image retrieval and classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Pattern Recognition Experiments in the Mandala/Cosine Domain
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient compressed domain target image search and retrieval
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Fast algorithms for the discrete cosine transform
IEEE Transactions on Signal Processing
Rotation-invariant texture classification using a complete space-frequency model
IEEE Transactions on Image Processing
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
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In this paper, some fast feature extraction algorithms are addressed for joint retrieval of images compressed in JPEG and JPEG2000 formats. In order to avoid full decoding, three fast algorithms that convert block-based discrete cosine transform (BDCT) into wavelet transform are developed, so that wavelet-based features can be extracted from JPEG images as in JPEG2000 images. The first algorithm exploits the similarity between the BDCT and the wavelet packet transform. For the second and third algorithms, the first algorithm or an existing algorithm known as multiresolution reordering is first applied to obtain bandpass subbands at fine scales and the lowpass subband. Then for the subbands at the coarse scale, a new filter bank structure is developed to reduce the mismatch in low frequency features. Compared with the extraction based on full decoding, there is more than 72% reduction in computational complexity. Retrieval experiments also show that the three proposed algorithms can achieve higher precision and recall than the multiresolution reordering, especially around the typical range of compression ratio.