Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 163,83
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. In.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 205,83
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. pp. 164.
Librería: preigu, Osnabrück, Alemania
EUR 140,00
Cantidad disponible: 5 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Perceptual Metrics for Image Database Navigation | Yossi Rubner (u. a.) | Taschenbuch | xxiii | Englisch | 2010 | Springer | EAN 9781441948632 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 167,14
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The increasing amount of information available in today's world raises the need to retrieve relevant data efficiently. Unlike text-based retrieval, where keywords are successfully used to index into documents, content-based image retrieval poses up front the fundamental questions how to extract useful image features and how to use them for intuitive retrieval. We present a novel approach to the problem of navigating through a collection of images for the purpose of image retrieval, which leads to a new paradigm for image database search. We summarize the appearance of images by distributions of color or texture features, and we define a metric between any two such distributions. This metric, which we call the 'Earth Mover's Distance' (EMD), represents the least amount of work that is needed to rearrange the mass is one distribution in order to obtain the other. We show that the EMD matches perceptual dissimilarity better than other dissimilarity measures, and argue that it has many desirable properties for image retrieval. Using this metric, we employ Multi-Dimensional Scaling techniques to embed a group of images as points in a two- or three-dimensional Euclidean space so that their distances reflect image dissimilarities as well as possible. Such geometric embeddings exhibit the structure in the image set at hand, allowing the user to understand better the result of a database query and to refine the query in a perceptually intuitive way.
Librería: Mispah books, Redhill, SURRE, Reino Unido
EUR 246,02
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: Like New. Like New. book.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 126,26
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: new. Questo è un articolo print on demand.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 160,49
Cantidad disponible: 2 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The increasing amount of information available in today's world raises the need to retrieve relevant data efficiently. Unlike text-based retrieval, where keywords are successfully used to index into documents, content-based image retrieval poses up front the fundamental questions how to extract useful image features and how to use them for intuitive retrieval. We present a novel approach to the problem of navigating through a collection of images for the purpose of image retrieval, which leads to a new paradigm for image database search. We summarize the appearance of images by distributions of color or texture features, and we define a metric between any two such distributions. This metric, which we call the 'Earth Mover's Distance' (EMD), represents the least amount of work that is needed to rearrange the mass is one distribution in order to obtain the other. We show that the EMD matches perceptual dissimilarity better than other dissimilarity measures, and argue that it has many desirable properties for image retrieval. Using this metric, we employ Multi-Dimensional Scaling techniques to embed a group of images as points in a two- or three-dimensional Euclidean space so that their distances reflect image dissimilarities as well as possible. Such geometric embeddings exhibit the structure in the image set at hand, allowing the user to understand better the result of a database query and to refine the query in a perceptually intuitive way. 164 pp. Englisch.
Librería: moluna, Greven, Alemania
EUR 136,16
Cantidad disponible: Más de 20 disponibles
Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The increasing amount of information available in today s world raises the need to retrieve relevant data efficiently. Unlike text-based retrieval, where keywords are successfully used to index into documents, content-based image retrieval poses up front th.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 216,54
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. Print on Demand pp. 164 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
Idioma: Inglés
Publicado por Springer, Springer Dez 2010, 2010
ISBN 10: 1441948635 ISBN 13: 9781441948632
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 160,49
Cantidad disponible: 1 disponibles
Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The increasing amount of information available in today's world raises the need to retrieve relevant data efficiently. Unlike text-based retrieval, where keywords are successfully used to index into documents, content-based image retrieval poses up front the fundamental questions how to extract useful image features and how to use them for intuitive retrieval. We present a novel approach to the problem of navigating through a collection of images for the purpose of image retrieval, which leads to a new paradigm for image database search. We summarize the appearance of images by distributions of color or texture features, and we define a metric between any two such distributions. This metric, which we call the 'Earth Mover's Distance' (EMD), represents the least amount of work that is needed to rearrange the mass is one distribution in order to obtain the other. We show that the EMD matches perceptual dissimilarity better than other dissimilarity measures, and argue that it has many desirable properties for image retrieval. Using this metric, we employ Multi-Dimensional Scaling techniques to embed a group of images as points in a two- or three-dimensional Euclidean space so that their distances reflect image dissimilarities as well as possible. Such geometric embeddings exhibit the structure in the image set at hand, allowing the user to understand better the result of a database query and to refine the query in a perceptually intuitive way.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 164 pp. Englisch.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 219,26
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. PRINT ON DEMAND pp. 164.