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Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework
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DOI：10.1109/TIP.2011.2108663.png)
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Abstract
En 中文
In this paper, we investigate a novel joint statistical model for subband coefficient magnitudes of the dual-tree complex wavelet transform, which is then coupled to a Bayesian framework for content-based image retrieval. The joint model allows to capture the association among transform coefficients of the same decomposition scale and different color channels. It further facilitates to incorporate recent research work on modeling marginal coefficient distributions. We demonstrate the applicability of the novel model in the context of color texture retrieval on four texture image databases and compare retrieval performance to a collection of state-of-the-art approaches in the field. Our experiments further include a thorough computational analysis of the main building blocks, runtime measurements, and an analysis of storage requirements. Eventually, we identify a model configuration with low storage requirements, competitive retrieval accuracy, and a runtime behavior, which enables the deployment even on large image databases.
Keywords:
Complex wavelet transform
copulas
image retrieval
statistical modeling
Journal
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13.7
Papers:
1.0W
Citations:
8.4W