Topic
Content-based image retrieval
About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.
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Papers
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01 Jan 2000
TL;DR: A Bayesian learning algorithm that relies on belief propagation to integrate feedback provided by the user over a retrieval session to show that significant improvements in the frequency of convergence to the relevant images can be achieved by the inclusion of learning in the retrieval process.
Abstract: We present a Bayesian learning algorithm that relies on belief propagation to integrate feedback provided by the user over a retrieval session. Bayesian retrieval leads to a natural criteria for evaluating local image similarity without requiring any image segmentation. This allows the practical implementation of retrieval systems where users can provide image regions, or objects, as queries. Region-based queries are significantly less ambiguous than queries based on entire images leading to significant improvements in retrieval precision. When combined with local similarity, Bayesian belief propagation is a powerful paradigm for user interaction. Experimental results show that significant improvements in the frequency of convergence to the relevant images can be achieved by the inclusion of learning in the retrieval process.
52 citations
TL;DR: This paper presents a novel approach for personal identification based on a wavelet-based fingerprint retrieval system which encompasses three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing.
52 citations
09 Jul 2007
TL;DR: Some of the main challenges facing trademark searchers today are outlined, and the extent to which current automated systems are meeting those challenges is discussed.
Abstract: In this paper, we outline some of the main challenges facing trademark searchers today, and discuss the extent to which current automated systems are meeting those challenges.
52 citations
29 Sep 2004
TL;DR: A robust probabilistic mixture model based on the multinomial and the Dirichlet distributions is presented and an unsupervised algorithm for learning this mixture is given.
Abstract: The performance of a statistical signal processing system depends in large part on the accuracy of the probabilistic model used. This paper presents a robust probabilistic mixture model based on the multinomial and the Dirichlet distributions. An unsupervised algorithm for learning this mixture is given, too. The proposed approach for estimating the parameters of the multinomial Dirichlet mixture is based on the maximum likelihood (ML) and Newton-Raphson methods. Experimental results involve improving content based image retrieval systems by integrating semantic features and by image database categorization
52 citations
TL;DR: The signature appears to capture perceptually relevant image features, in that it allows successful database querying using example images which have been subject to arbitrary camera and subject motion, and confirms invariance to 2D rigid transformations, as well as high resilience to more general affine and projective transformations.
52 citations