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Träfflista för sökning "WFRF:(Tran Linh) srt2:(2001-2004)"

Sökning: WFRF:(Tran Linh) > (2001-2004)

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1.
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2.
  • Tran Viet, Linh, 1973- (författare)
  • Statistical tools for color based image retrieval
  • 2001
  • Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Color has been widely used in content-based image retrieval applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. A distance measure is then used to measure the similarity between images based on the descriptions of their color distributions. In this thesis we develop statistical methods which focus on the representation of such distributions and distance measures between distributions.The distance measures are described in a differential geometry based frame­ work. This allows the incorporation of geometrical features of the underlying color space into the distance measure between the probability distributions. The general framework is illustrated with two examples: Normal distributions and linear representations of distributions. The linear representation of color distributions is used to derive new compact descriptors for color based image retrieval. These descriptors are based on the combination of two ideas: Integration of color information into the principal component computation and using local differences of histograms instead of all histograms for the estimation of the principal components. In our experiments we used several image databases containing about 200000 images. The experiments show that the method developed in this thesis is very fast and that the retrieval performance achieved compares favorably with existing methods.We also describe illumination invariant descriptors that can be used in, image database searches which retrieves images of objects independent of the illumination conditions under which these images were taken. We develop and investigate a moment based method for color image normalization and compare it to traditional color constancy methods.In the last chapter of the thesis we describe an industrial application of some of the ideas described in the first part of the thesis. In this application we use color correction methods to optimize the layout of a newspaper page.
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3.
  • Viet Tran, Linh (författare)
  • Efficient Image Retrieval with Statistical Color Descriptors
  • 2003
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • Color has been widely used in content-based image retrieval (CBIR) applications. In such applications the color properties of an image are usually characterized by the probability distribution of the colors in the image. A distance measure is then used to measure the (dis-)similarity between images based on the descriptions of their color distributions in order to quickly find relevant images. The development and investigation of statistical methods for robust representations of such distributions, the construction of distance measures between them and their applications in efficient retrieval, browsing, and structuring of very large image databases are the main contributions of the thesis. In particular we have addressed the following problems in CBIR.Firstly, different non-parametric density estimators are used to describe color information for CBIR applications. Kernel-based methods using nonorthogonal bases together with a Gram-Schmidt procedure and the application of the Fourier transform are introduced and compared to previously used histogram-based methods. Our experiments show that efficient use of kernel density estimators improves the retrieval performance of CBIR. The practical problem of how to choose an optimal smoothing parameter for such density estimators as well as the selection of the histogram bin-width for CBIR applications are also discussed.Distance measures between color distributions are then described in a differential geometry-based framework. This allows the incorporation of geometrical features of the underlying color space into the distance measure between the probability distributions. The general framework is illustrated with two examples: Normal distributions and linear representations of distributions. The linear representation of color distributions is then used to derive new compact descriptors for color-based image retrieval. These descriptors are based on the combination of two ideas: Incorporating information from the structure of the color space with information from images and application of projection methods in the space of color distribution and the space of differences between neighboring color distributions. In our experiments we used several image databases containing more than 1,300,000 images. The experiments show that the method developed in this thesis is very fast and that the retrieval performance chievedcompares favorably with existing methods. A CBIR system has been developed and is currently available at http://www.media.itn.liu.se/cse.We also describe color invariant descriptors that can be used to retrieve images of objects independent of geometrical factors and the illumination conditions under which these images were taken. Both statistics- and physics-based methods are proposed and examined. We investigated the interaction between light and material using different physical models and applied the theory of transformation groups to derive geometry color invariants. Using the proposed framework, we are able to construct all independent invariants for a given physical model. The dichromatic reflection model and the Kubelka-Munk model are used as examples for the framework.The proposed color invariant descriptors are then applied to both CBIR, color image segmentation, and color correction applications. In the last chapter of the thesis we describe an industrial application where different color correction methods are used to optimize the layout of a newspaper page.
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