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- W55184434 abstract "We introduce a spatial color image retrieval method which does not include any segmentation step. This method relies on small image thumbnails and the Earth Mover’s Distance (EMD). We then derive an unsupervised matching criterion using an a contrario approach. Experiments are performed on a database of illuminated manuscripts. Introduction Color image retrieval methods that do not take into account any spatial layout miss important cues of human visual perception. Hence spatial color indexing retrieval has become a very active research area. Some approaches augment histograms with pixel localization or spatial color correlation, see e.g. [1, 2, 3, 4]. Other methods use points of interest in the image to include spatial features [5, 6]. Region-based approaches first perform a segmentation of images which is then used for image comparison. The Blobworld image representation, for instance, uses multidimensional Gaussian mixtures as the image model [7]. Many other unsupervised segmentation methods have been used to index images, see e.g. [8, 9, 10]. A matching step follows the segmentation and indexing steps, generally relying on a distance or a similarity measure. One of the most efficient distance to compare sets of features is the Earth Mover’s Distance (EMD) [11], see e.g. [12, 8]. We propose a retrieval method that rely on the spatial organization of colors. The plan of the paper as follows. We first define the features used to represent the spatial and color contents of images. We then introduce a distance derived from optimal transportation problems, the Earth Mover’s Distance, and discuss its use for the comparison of images signatures. Next we derive an automatic criterion, enabling to decide whether two images should be matched or not, using an a contrario approach. We conclude with numerical experiments on a database of illuminated manuscripts. Features The proposed method uses coarsely sampled thumbnails to represent images (Fig. 1). The total number of pixels of these thumbnails is fixed to n, depending on the database content and on computing constraints. Image subsampling is achieved by averaging the image pixels. We choose the psychometric CIELab color space to represent thumbnails, in order to use the Euclidean distance to compare their color components. The signature for an image is composed of the n thumbnail pixel features f i = {Li,ai,bi,X i,Y i}, where L,a,b are the color coordinates and X ,Y the spatial positions. Distance To measure the distance between two pixel features f i Q and f j T of a query image Q and a target image T respectively, a Figure 1. A coarsely sampled thumbnail. Left: source image (3200×2200 pixels). Right: the thumbnail (10×15 pixels). weighted exponential distance is used: de( f i Q, f j T ) = α ∗ 1− e − ( (Li Q−L j T ) +(aQ−a j T ) +(bQ−b j T ) 2" @default.
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- W55184434 date "2006-01-01" @default.
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- W55184434 title "Spatial color image retrieval without segmentation using thumbnails and the Earth Mover's Distance" @default.
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