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Image and Image-Set Modeling Using a Mixture Model

In: Compstat 2008

Author

Listed:
  • Charbel Julien

    (Université Lumière Lyon2, Laboratoire ERIC)

  • Lorenza Saitta

    (Università del Piemonte Orientale, Dipartimento di Informatica)

Abstract

Modeling an image or an image-set, which share similar visual contents, by means of a discrete distribution (such as a signature) or by means of a mixture model (such as a Gaussian mixture-model) has a major utility, and may serve as a basis for Content Based Image Retrieval and other related areas. Mixture model can encode information about color, texture, and spatial relationships between colored/textured regions. Image modeling is used in several tasks, such as Image retrieval, Automatic annotation, Unsupervised or Semi-supervised Clustering. Linear optimization techniques offer a reliable and efficient way to compute distance, in both cases, discrete distributions and mixture models. Linear optimization can be also used for modeling image-sets, by computing a mixture model that minimizes distances.

Suggested Citation

  • Charbel Julien & Lorenza Saitta, 2008. "Image and Image-Set Modeling Using a Mixture Model," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 267-275, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2084-3_22
    DOI: 10.1007/978-3-7908-2084-3_22
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