Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Learning image semantics from users relevance feedback
Proceedings of the 12th annual ACM international conference on Multimedia
Relevance feedback using adaptive clustering for image similarity retrieval
Journal of Systems and Software
Computer Vision and Image Understanding
Kernel MDL to Determine the Number of Clusters
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Deterministic annealing EM and its application in natural image segmentation
CIS'04 Proceedings of the First international conference on Computational and Information Science
A relevance feedback approach for content based image retrieval using gaussian mixture models
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Neural network based image retrieval with multiple instance leaning techniques
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Online image retrieval system using long term relevance feedback
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
Active concept learning in image databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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The standard approach to content-based image retrieval is currently concerned with bridging the semantic gap or the gap between the results produced by the use of low-level features and the human end-user expectations based on high-level semantics. In this paper, we suggest that there are advantages to bridging the gap in two stages by proposing an intermediate level. We show that unsupervised clustering of low-level image features provides a suitable basis for an intermediate level representation and define a CBIR system using such an approach. The main advantages of using an intermediate level are (a) it is not necessary for all positive responses to a user query be categorized into a single class; (b) it is possible to overcome the small-sample problem with too few positive examples; and, (c) to improve performance without greatly increased computational cost. Experimental results on Wang's database (1000 images) and Corel Photo gallery (10,800 images) show that the intermediate level analysis leads to better results.