Improved stochastic modeling of shapes for content-based image retrieval

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Abstract
Recent advances in the stochastic modeling of shapes for content-based image database retrieval are presented in this paper. These advances include an integrated approach to shape and color-based retrieval, where the cues color and shape are both utilized in a local rather than a global way, as well as a novel deformation tolerant method based on (pseudo-) two-dimensional stochastic models. The stochastic modeling itself is based on the use of HMMs, whereas the feature extraction is a polar sampling technique which is also known as shape matrix. In an earlier publication, it has been demonstrated that this combination of feature extraction and HMMs is able to perform an elastic matching, which is especially needed in sketch based image retrieval. The use of streams (sets of features that are assumed to be statistically independent) within the HMM framework allows the integration of shape and color derived features into a single model, thereby allowing to control the influence of the different streams via stream weights. Furthermore, these stream weights can also be utilized in order to integrate weighting factors, which have been derived in the context of shape matrices, in order to achieve a more objective comparison between shapes. The weighting factors are based on the fact that the sampling density is not constant with the polar sampling raster.

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