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Book Sections Year : 2017

Perceptual Texture Similarity for Machine Intelligence Applications

Abstract

Textures are homogeneous visual phenomena commonly appearing in the visual scene. They are usually characterized by randomness with some stationar-ity. They have been well studied in different domains, such as neuroscience, vision science and computer vision, and showed an excellent performance in many applications for machine intelligence. This book chapter focuses on a special analysis task of textures for expressing texture similarity. This is quite a challenging task, because the similarity highly deviates from point-wise comparison. Texture similarity is key tool for many machine intelligence applications, such as recognition, classification, synthesis and etc. The chapter review the theories of texture perception, and provides a survey about the up-to-date approaches for both static and dynamic textures similarity. The chapter focuses also on the special application of texture similarity in image and video compression, providing the state of the art and prospects
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Dates and versions

hal-01645011 , version 1 (22-11-2017)

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  • HAL Id : hal-01645011 , version 1

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Karam Naser, Vincent Ricordel, Patrick Le Callet. Perceptual Texture Similarity for Machine Intelligence Applications. Visual Content Indexing and Retrieval with Psycho-Visual Models, 2017. ⟨hal-01645011⟩
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