Abstract: Multimedia information retrieval is performed using meta-descriptors in addition to descriptors. A “descriptor” is a representation of a feature, a “feature” being a distinctive characteristic of multimedia information, while a “meta-descriptor” is information about the descriptor. Meta-descriptors are generated for multimedia information in a repository (10, 12, 14, 16, 18, 20, 22, 24) by extracting the descriptors from the multimedia information (111), clustering the multimedia information based on the descriptors (112), assigning meta-descriptors to each cluster (113), and attaching the meta-descriptors to the multimedia information in the repository (114).

Abstract: An image retrieval system contains a database with a large number of images. The system retrieves images from the database that are similar to a query image entered by the user. The images in the database are grouped in clusters according to a similarity criterion so that mutually similar images reside in the same cluster. Each cluster has a cluster center which is representative for the images in it. A first step of the search to similar images selects the clusters that may contain images similar with the query image, by comparing the query image with the cluster centers of all clusters. A second step of the search compares the images in the selected clusters with the query image in order to determine their similarity with the query image.

Abstract: Multimedia information retrieval is performed using meta-descriptors in addition to descriptors. A “descriptor” is a representation of a feature, a “feature” being a distinctive characteristic of multimedia information, while a “meta-descriptor” is information about the descriptor. Meta-descriptors are generated for multimedia information in a repository (10, 12, 14, 16, 18, 20, 22, 24) by extracting the descriptors from the multimedia information (111), clustering the multimedia information based on the descriptors (112), assigning meta-descriptors to each cluster (113), and attaching the meta-descriptors to the multimedia information in the repository (114).

Abstract: There is disclosed, for use in a video editing system, a video processing device capable of receiving a first video clip containing at least one shot (or scene) consisting of a sequence of uninterrupted related frames and performing fast forward or slow motion special effects that vary according to the activity level in the shot. The video processing device comprises an image processor capable of identifying the shot and determining a first activity level within at least a portion of the shot. The image processor then performs the selected speed change special effect by adding frames or deleting frames in the first portion in response to the activity level determination, thereby producing a modified shot.

Abstract: An image retrieval system contains a database with a large number of images. The system retrieves images from the database that are similar to a query image entered by the user. The images in the database are grouped in clusters according to a similarity criterion so that mutually similar images reside in the same cluster. Each cluster has a cluster center which is representative for the images in it. A first step of the search to similar images selects the clusters that may contain images similar with the query image, by comparing the query image with the cluster centers of all clusters. A second step of the search compares the images in the selected clusters with the query image in order to determine their similarity with the query image.

Abstract: Multimedia information retrieval is performed using meta-descriptors in addition to descriptors. A “descriptor” is a representation of a feature, a “feature” being a distinctive characteristic of multimedia information, while a “meta-descriptor” is information about the descriptor. Meta-descriptors are generated for multimedia information in a repository (10, 12, 14, 16, 18, 20, 22, 24) by extracting the descriptors from the multimedia information (111), clustering the multimedia information based on the descriptors (112), assigning meta-descriptors to each cluster (113), and attaching the meta-descriptors to the multimedia information in the repository (114).

Abstract: An image retrieval system contains a database with a large number of images. The system retrieves images from the database that are similar to a query image entered by the user. The images in the database are grouped in clusters according to a similarity criterion so that mutually similar images reside in the same cluster. Each cluster has a cluster center which is representative for the images in it. A first step of the search to similar images selects the clusters that may contain images similar with the query image, by comparing the query image with the cluster centers of all clusters. A second step of the search compares the images in the selected clusters with the query image in order to determine their similarity with the query image.

Abstract: In an image retrieval system, a database with a large number of images is searched to find one or more images meeting the specification of a user. This specification is given in the form of a query image. The system determines the similarity between the query image and a particular image from the database by comparing the color histograms of the two images. The histograms are treated as statistical distributions and the similarity is determined on the basis of an information theoretic measure of the distributions. In a first embodiment, the similarity is determined using the Kullback informational divergence of the two histograms. In a second embodiment, the similarity is based on the entropy of the distribution of similarity coefficients of the two histograms is used.

Abstract: Signatures from MPEG or Motion JPEG encoded video clips are extracted based on the luminance, chrominance, and motion vector values. The signatures are stored in a database, along with corresponding location, size, and time length information. The signature of a query video clip, also encoded in the MPEG or Motion JPEG format, is extracted. The signature of the query video clip is compared with the signatures stored in the meta database, with video clips having signatures similar to the signature of the query video clip identified. The video clips are then retrieved and displayed by selection of a user.

Abstract: A computer-implemented method of identifying suspect masses in digital radiologic images and a system for computer-aided diagnosis of such images in which the images are thresholded at a large number of threshold levels to discriminate spots and a two stage classifier is applied to the spots. The first classification stage applies multiple rules predetermined from a training set of images, to a relatively computationally inexpensive set of initial features, namely area, compactness, eccentricity, contrast, and intensity variance for each spot. More computationally expensive features, namely edge orientation distribution and texture features, are computed only for spots that are accepted by the first classification stage to points for these spots in an expanded feature space.