International Journal of Computer Vision
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Image Retrieval on Compressed Domain with Q-Distance
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
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
Applying the extended mass-constraint EM algorithm to image retrieval
Computers & Mathematics with Applications
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
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We consider a hypothesis testing approach to content-based image retrieval (CBIR) using Discrete Cosine Transform (DCT) coefficients restored by partially decoding JPEG images. In order to further decorrelate DC coefficients from an image, a 2 × 2 DCT is performed on the sub-image constructed from all the DC coefficients. Assume that each DCT coefficient sequence is emitted from a memoryless source, and all these sources are independent of each other. For each target image we form a hypothesis that its DCT coefficient sequences are emitted from the same sources as the corresponding sequences in the query image. Testing these hypotheses by measuring the log-likelihoods leads to a simple yet efficient scheme that ranks each target image according to the Kullback-Leibler (KL) divergence between the empirical distribution of the DCT coefficient sequences in the query image and that in the target image. Experiments on two image datasets show that our approach achieves consistently better retrieval results than related methods in the literature.