Abstract: A method describes activity in a video sequence. The method measures intensity, direction, spatial, and temporal attributes in the video sequence, and the measured attributes are combined in a digital descriptor of the activity of the video sequence.

Abstract: A color image segmentation method is provided. The color image segmentation method includes the steps of: (a) calculating a first value representing the degree of difference between a pixel and the color of peripheral pixels based a plurality of pixel values of an input image; (b) obtaining a converted image by converting the first calculated value into a value of a predetermined scale; and (c) segmenting the converted image. According to the color image segmentation method, an effective and an automatic segmentation is possible, and a segmentation speed is high even when segmenting an image containing much noise.

Abstract: A color image processing method and apparatus thereof are provided. The color image processing method includes the steps of (a) sorting image pixels according to the color distance between the image pixels and a central pixel, (b) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (c) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups. The color image processing method can reduce the generation of edge blurring of the image in removing impulse noise from an image and filtering the same.

Abstract: A color image processing method for retrieving a color feature descriptor for describing color features of an image is provided. The color image processing method includes the steps of (a) obtaining color vectors of an input image, (b) classifying the color vectors to obtain dominant colors of the input image and the ratios thereof, and (c) representing the dominant colors and the ratios thereof as a color feature descriptor of the input image. The color image processing method is applied to an object-based image processing method, thereby allowing fast search and retrieval of multi-media contents.

Abstract: A color image processing method for retrieving a color feature descriptor for describing color features of an image is provided. The color image processing method includes the steps of (a) obtaining color vectors of an input image, (b) classifying the color vectors to obtain dominant colors of the input image and the ratios thereof, and (c) representing the dominant colors and the ratios thereof as a color feature descriptor of the input image. The color image processing method is applied to an object-based image processing method, thereby allowing fast search and retrieval of multi-media contents.

Abstract: A color image processing method for retrieving a color feature descriptor for describing color features of an image is provided. The color image processing method includes the steps of (a) obtaining color vectors of an input image, (b) classifying the color vectors to obtain dominant colors of the input image and the ratios thereof, and (c) representing the dominant colors and the ratios thereof as a color feature descriptor of the input image. The color image processing method is applied to an object-based image processing method, thereby allowing fast search and retrieval of multi-media contents.

Abstract: A database building method for multimedia contents is provided. The database building method for multimedia contents has the steps of (a) accessing an arbitrary site providing multimedia contents through a telecommunications network; (b) calling multimedia contents in by spidering the site; and (c) classifying the multimedia contents data according to the stored addresses and storing them in a predetermined database. Using category information on the corresponding sites, the database building method for multimedia contents according to the present invention semantically classifies multimedia contents and stores them in the corresponding databases. In the database built by the database building method for multimedia contents according to the present invention, multimedia contents which are dispersed on the WWW are well collected and, using category information or URL information, are semantically well classified.

Abstract: A method describes activity in a video sequence. The method measures intensity, direction, spatial, and temporal attributes in the video sequence, and the measured attributes are combined in a digital descriptor of the activity of the video sequence.

Abstract: A database building method for multimedia contents is provided. The database building method for multimedia contents has the steps of (a) accessing an arbitrary site providing multimedia contents through a telecommunications network; (b) calling multimedia contents in by spidering the site; and (c) classifying the multimedia contents data according to the stored addresses and storing them in a predetermined database. Using category information on the corresponding sites, the database building method for multimedia contents according to the present invention semantically classifies multimedia contents and stores them in the corresponding databases. In the database built by the database building method for multimedia contents according to the present invention, multimedia contents which are dispersed on the WWW are well collected and, using category information or URL information, are semantically well classified.

Abstract: An object activity modeling method which can efficiently model complex objects such as a human body is provided. The object activity modeling method includes the steps of (a) obtaining an optical flow vector from a video sequence; (b) obtaining the probability distribution of the feature vector for a plurality of video frames, using the optical flow vector; (c) modeling states, using the probability distribution of the feature vector; and (d) expressing the activity of the object in the video sequence based on state transition. According to the modeling method, in video indexing and recognition field, complex activities such as human activities can be efficiently modeled and recognized without segmenting objects.

Abstract: An image processing method wherein a “peer group” is identified for each pixel in the image and the peer group is then used to make processing decisions at that pixel. A diffusion form of peer group processing is presented that averages over the peer group (PGA). Two parameters provide direct control over which objects are selectively enhanced: area (number of pixels in the object) and window diameter (window size needed to enclose the object). The direct correspondence between these parameters and the object characteristics is in sharp contrast to parameter selection for other image processing methods such as variational approaches in which it is unclear how to select good values for the smoothness and approximation weights. The scale space decomposition of an image determined by using the peer group number as the scale parameter leads to top hat procedures for recovering objects in a given size range.

Abstract: A method of measuring a similarity between images is provided. The method includes the steps of (a) computing a statistical dissimilarity between the images; (b) computing a perceptual dissimilarity between the images; and (c) computing a dissimilarity between the texture features of the images based on the statistical dissimilarity and the perceptual dissimilarity. When images perceptually similar to a query image are searched and retrieved according to the above measuring method, the possibility that the retrieved images are not perceptually similar to the query image is small. Accordingly, the image searching performance can be improved when searching images having texture features similar to that of a query image.

Abstract: An indexing method of a feature vector data space, which can be used for a similarity search in a multidimensional vector space, is provided. The indexing method includes the steps of (a) determining whether at least one cell, on which feature vectors are concentrated, exists, and (b) hierarchically indexing the feature vector data space when it is determined that at least one cell, on which feature vectors are concentrated, exists in the step (a). Accordingly, the feature vector data space can be finely indexed when feature vectors are not uniformly distributed in a high-dimensional vector space.