Yossi Rubner – författare
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3 produkter
3 produkter
Inbunden, Engelska, 2000
1 616 kr
Skickas inom 10-15 vardagar
With the increasing number of images available electronically, automatic retrieval systems are becoming essential. This book introduces a useful tool for any such system: a metric, called the Earth Mover's Distance (EMD), for comparing images in terms of their appearance. This metric describes the amount of work that is necessary to transform one image into another, in a precisely defined mathematical sense, and in a flexible and perceptually meaningful manner. An efficient linear programming algorithm enables the computation of this metric fast enough to be used for the interactive retrieval of images from large repositories. The perceptual properties of the EMD, and the speed of its computation, lead to database navigation, a new paradigm for interacting with a repository of images. When navigating, the user is shown a very large number of images in response to a query. The EMD between pairs of images, together with a multidimensional scaling method, allows these images to be displayed so that similar images appear near to each other on the computer screen.In this way, the user can grasp at a glance what is returned, and can reach the images of interest with a small number of mouse clicks. A CD-ROM with full colour images is included. Extensive benchmark evaluations and example retrieval systems show the usefulness of the EMD and the advantages of image database navigation.
Häftad, Engelska, 2010
1 616 kr
Skickas inom 10-15 vardagar
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.
E-bok
PDF, Engelska, 20132 049 kr
Läs direkt efter köp
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.