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Content Based Image Retrieval by Thepade Sudeep (4 results)

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  • Language: English

    Published by LAP Lambert Academic Publishing, 2012

    3847341251 / 9783847341253

    • Softcover

    Seller: preigu, Osnabrück, Germanypreigu

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    Taschenbuch. Condition: Neu. Content Based Image Retrieval | New Approaches of Feature Vector Extraction | Sudeep D. Thepade (u. a.) | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783847341253 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

  • Language: English

    Published by LAP LAMBERT Academic Publishing, 2012

    3847341251 / 9783847341253

    • Softcover

    Seller: Mispah books, Redhill, SURRE, United KingdomMispah books

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  • Language: English

    Published by LAP LAMBERT Academic Publishing, 2012

    3847341251 / 9783847341253

    • Softcover
    • Print on Demand

    Seller: moluna, Greven, Germanymoluna

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    Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Thepade Sudeep D.Dr. Sudeep D. Thepade is Ph.D. (Computer Engineering), M.E.(Computer Engineering),B.E.(Computer). He has more than 130 papers in National/International Conferences/Journals to his credit with many accolades and award.…

  • Language: English

    Published by LAP Lambert Academic Publishing, 2012

    3847341251 / 9783847341253

    • Softcover
    • Print on Demand

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modern image search engines retrieve the images based on their visual contents, commonly referred to as Content Based Image Retrieval (CBIR) systems. Typical CBIR systems can organize and retrieve images from image databases, automatically by extracting some features such as color, texture, shape from images and looking for similar images which have similar feature. One problem of this approach is reliance on visual similarity to judge semantic similarity, which creates problems due to semantic gap between low-level content and high level concepts. Even with the subsistence of this problem, if aggressive attempts are made CBIR can be used for real life applications. For example in spite of the open problems like robust text understanding, Google and Yahoo have become most popular for searching. The work presented here mainly focuses on efficient CBIR methods with help of representation of converting the visual content of images in feature vector using proposed techniques. The proposed CBIR methods using Colour, Transformed Image, Texture and Shape content are proved to be better and faster using test bed of 1000 variable size images spread across 11 image categories.…