An user preference information based kernel for SVM active learning in content-based image retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Weighted pseudo-metric for a fast CBIR method
Machine Graphics & Vision International Journal
Applying the extended mass-constraint EM algorithm to image retrieval
Computers & Mathematics with Applications
Sequential organization of speech in computational auditory scene analysis
Speech Communication
Cov-HGMEM: an improved hierarchical clustering algorithm
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Some question to Monte-Carlo simulation in AIB algorithm
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Lightweight probabilistic texture retrieval
IEEE Transactions on Image Processing
Histogram similarity measure using variable bin size distance
Computer Vision and Image Understanding
Unsupervised image retrieval framework based on rule base system
Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
Using manual and automated annotations to search images by semantic similarity
Multimedia Tools and Applications
Concept-based indexing of annotated images using semantic DNA
Engineering Applications of Artificial Intelligence
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Probabilistic approaches are a promising solution to the image retrieval problem that, when compared to standard retrieval methods, can lead to a significant gain in retrieval accuracy. However, this occurs at the cost of a significant increase in computational complexity. In fact, closed-form solutions for probabilistic retrieval are currently available only for simple probabilistic models such as the Gaussian or the histogram. We analyze the case of mixture densities and exploit the asymptotic equivalence between likelihood and Kullback-Leibler (KL) divergence to derive solutions for these models. In particular, 1) we show that the divergence can be computed exactly for vector quantizers (VQs) and 2) has an approximate solution for Gauss mixtures (GMs) that, in high-dimensional feature spaces, introduces no significant degradation of the resulting similarity judgments. In both cases, the new solutions have closed-form and computational complexity equivalent to that of standard retrieval approaches.