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In this paper, a multimodal image retrieval framework integrating the information in both audio and visual domain via Bayesian decision level fusion is proposed. In both domains, a statistical model for each semantic class is learned. Based on the Bayes' theorem, the a posteriori probability of each class given a query is calculated in the audio domain, which is propagated to the images classified...
The overall objective of this paper is to present n methodology for reducing the human workload through adapting an automatic scheme for content-based image retrieval (CBIR) engines. The proposed system utilizes an unsupervised hierarchical clustering algorithm, known as the directed self-organizing tree map (DSOTM) that aims to closely mimic the process of information classification thought to be...
The main focus of this paper is to present a methodology for optimizing relevance identification in content-based image retrieval (CBIR) systems through the principle of feature weight detection. The purpose of relevance identification is to find a collection of images that are statistically similar to, or match with, an original query image within a large visual database. The novelty of this scheme...
In this work, we present a modified version of the generic Fourier descriptor (GFD) that operates on edge information within natural images from the COREL image database for the purpose of shape-based image retrieval. By incorporating an edge-texture characterization (ETC) measure, we reduced the complexity inherent in oversensitive edge maps typical of most gradient-based detectors that otherwise...
The overall objective of this paper is to present a methodology for guiding adaptations of an RBF based relevance feedback network, embedded in automatic content-based image retrieval (CBIR) systems, through the principle of unsupervised hierarchical clustering. The self organizing tree map (SOTM) is essentially attractive for our approach since it not only extracts global intuition from an input...
Two fundamental aspects for content-based image retrieval system are studied: visual feature extraction and retrieval system design. (i) Feature extraction shares some common properties with image compression where the multiresolution nature of wavelet decomposition can be exploited. When wavelet coefficients are vector quantized, the information content in each spatial-frequency subband is mapped...
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