Abstract: A digital video processing method and an apparatus thereof are provided. The method for processing digital images received in the form of compressed video streams comprising the step of determining a region intensity histogram (RIH) based on information on motion compensation of inter frames. The RIH information is obtained based on the motion compensation values of inter frames, and the RIH information is a good indicator of motion information of a video scene. Also, since the RIH information is quite a good indicator of intensity of the video scene, video streams having similar intensities can be effectively searched by searching for similar video scenes based on the RIH information obtained by the digital video processing method.

Abstract: A method is provided for indexing feature vectors within feature vector data space which includes adaptively forming approximation of the feature vectors on the basis of statistical distribution of feature vector data within the feature vector data space. The method for indexing feature vector data space of the present invention efficiently indexes vector space having high-dimensionality in which generally feature vectors are not uniformly distributed. Also, the method for indexing feature vector data space has an advantage that indexing can be easily upgraded when new feature vector data is added.

Abstract: An adaptive search method in feature vector space which can quickly search the feature vector space indexed based on approximation for a feature vector having features similar to a query vector according to a varying distance measurement is provided. The adaptive search method includes the steps of (a) performing a similarity measurement on a given query vector within the feature vector space, and (b) applying search conditions limited by the result of the similar measurement obtained in the step (a) and performing a changed similarity measurement on the given query vector. According to the adaptive search method, the number of candidate approximation regions is reduced during a varying distance measurement such as an on-line retrieval, which improves the search speed.

Abstract: A digital image texture analyzing method including the steps of (a) obtaining the mean (?0) and variance (?0) of the pixel values of an original image, and (b) obtaining m×n filtered images by filtering the original image using predetermined filters, each having a unique combination of one of m scales and one of n orientations, where m and n are predetermined positive integers, (c) calculating the means (?11, ?21, . . . , ?mn) and variances (?11, ?21, . . . , ?mn) of the respective filtered images, and (d) obtaining a texture descriptor having the mean (?0) and variance (?0) of the pixel values of the original image obtained in the step (a), and the means (?11, ?21, . . . , ?mn) and variances (?11, ?21, . . . , ?mn) of the respective filtered images obtained in the step (c), as texture features. The texture analyzing method allows image textures to be more accurately analyzed and compared.

Abstract: A method of indexing a high-dimensional vector space, along with a method of quickly retrieving a feature vector having features similar to a query vector from the vector space indexed by the indexing method, are provided. The method of indexing a feature vector space includes the steps of (a) partitioning the feature vector space into a plurality of approximation regions; (b) selecting an arbitrary approximation region to determine whether the selected approximation region is heavily or sparsely distributed; and (c) if the approximation region is determined to be sparsely distributed, indexing the corresponding approximation region as one special node belonging to a child node of the tree data structure, together with any other sparsely distributed approximation region spaced apart by a distance less than a predetermined distance.

Abstract: A method of indexing a high-dimensional vector space, along with a method of quickly retrieving a feature vector having features similar to a query vector from the vector space indexed by the indexing method, are provided. The method of indexing a feature vector space includes the steps of (a) partitioning the feature vector space into a plurality of approximation regions; (b) selecting an arbitrary approximation region to determine whether the selected approximation region is heavily or sparsely distributed; and (c) if the approximation region is determined to be sparsely distributed, indexing the corresponding approximation region as one special node belonging to a child node of the tree data structure, together with any other sparsely distributed approximation region spaced apart by a distance less than a predetermined distance.

Abstract: A digital video processing method and an apparatus thereof are provided. The method for processing digital images received in the form of compressed video streams comprising the step of determining a region intensity histogram (RIH) based on information on motion compensation of inter frames. The RIH information is obtained based on the motion compensation values of inter frames, and the RIH information is a good indicator of motion information of a video scene. Also, since the RIH information is quite a good indicator of intensity of the video scene, video streams having similar intensities can be effectively searched by searching for similar video scenes based on the RIH information obtained by the digital video processing method.

Abstract: A digital video processing method and an apparatus thereof are provided. The method for processing digital images received in the form of compressed video streams comprising the step of determining a region intensity histogram (RIH) based on information on motion compensation of inter frames. The RIH information is obtained based on the motion compensation values of inter frames, and the RIH information is a good indicator of motion information of a video scene. Also, since the RIH information is quite a good indicator of intensity of the video scene, video streams having similar intensities can be effectively searched by searching for similar video scenes based on the RIH information obtained by the digital video processing method.

Abstract: A color image processing method is provided. The color image processing method includes the step of: (a) indexing a color image by assigning representative colors of an image to a color space divided into a plurality of regions. The color image processing method may be applied to object-based image processing such as MPEG-7, and fast search and retrieval of multimedia content can be made.

Abstract: A digital image segmentation method and device is provided. The method includes the steps of grouping an image stream into a plurality of continuous frame groups, setting one arbitrary frame as an I frame and the others as P frames in each group, segmenting the I frame into a plurality of regions, and segmenting the P frames by obtaining motion vectors, using motion prediction, of the segmented I frame regions, respectively, with respect to each of the P frames, by moving the segmented I frame regions by the respective motion vectors, and by setting the moved I frame regions as segmented P frame regions. Accordingly, the segmentation of the I frame is applied to the P frame segmentation, and a conventional segmentation process consuming excessive time with respect to all frames does not need to be performed. Thus, rapid segmentation can be performed.

Abstract: A method of indexing a high-dimensional vector space, along with a method of quickly retrieving a feature vector having features similar to a query vector from the vector space indexed by the indexing method, are provided. The method of indexing a feature vector space includes the steps of (a) partitioning the feature vector space into a plurality of approximation regions; (b) selecting an arbitrary approximation region to determine whether the selected approximation region is heavily or sparsely distributed; and (c) if the approximation region is determined to be sparsely distributed, indexing the corresponding approximation region as one special node belonging to a child node of the tree data structure, together with any other sparsely distributed approximation region spaced apart by a distance less than a predetermined distance.

Abstract: An adaptive search method in feature vector space which can quickly search the feature vector space indexed based on approximation for a feature vector having features similar to a query vector according to a varying distance measurement is provided. The adaptive search method includes the steps of (a) performing a similarity measurement on a given query vector within the feature vector space, and (b) applying search conditions limited by the result of the similar measurement obtained in the step (a) and performing a changed similarity measurement on the given query vector. According to the adaptive search method, the number of candidate approximation regions is reduced during a varying distance measurement such as an on-line retrieval, which improves the search speed.

Abstract: An apparatus for searching a digital image and a method therefor are provided. The digital image search apparatus includes a database processing unit for segmenting an image from the database or an image which is not stored in the database into homogeneous regions and analyzing and storing the segmented regions and an image search engine unit for searching for an image having a segmented region showing a characteristic similar to the characteristic of the query region. It is possible to perform detailed and efficient searching work since it is possible to perform a region-based search by means of segmented regions and a user can perform searching by selecting the characteristic and the position of the segmented regions.

Abstract: A method is provided for indexing feature vectors within feature vector data space which includes adaptively forming approximation of the feature vectors on the basis of statistical distribution of feature vector data within the feature vector data space. The method for indexing feature vector data space of the present invention efficiently indexes vector space having high-dimensionality in which generally feature vectors are not uniformly distributed. Also, the method for indexing feature vector data space has an advantage that indexing can be easily upgraded when new feature vector data is added.

Abstract: A method for digital watermarking and, in particular, for digital data hiding of significant amounts of data in images and video. The method employs a discrete wavelet transform for embedding gray scale images which can be as great as 25% of the host image data. A simple control parameter is used that can be tailored to either hiding or watermarking purposes, and is robust to operations such as JPEG compression. The method also uses noise-resilient channel codes based on multidimensional lattices which can provide for embedding signature data such as gray-scale or color images. Furthermore, embedded image data can be recovered in the absence of the original host image by inserting the data into the host image in the DCT domain by encoding the signature DCT coefficients using a lattice coding scheme before embedding, checking each block of host DCT coefficients for its texture content, and appropriately inserting the signatured codes depending on a local texture measure.

Abstract: A texture description method in a frequency domain for extracting texture features by transforming and Gabor-filtering an input image into an image of the frequency domain, and a texture-based retrieval method thereof are provided. The texture description method in the frequency domain includes: a first step of transforming an image of a time domain into an image of the frequency domain; a second step of filtering the transformed image using a Gabor filter having N×M filter regions, where N and M are respective predetermined positive integers; a third step of extracting feature values of the image that has been Gabor-filtered in respective channels of the frequency domain division layout corresponding to the N×M filter regions of the Gabor filter; and a fourth step of deciding a texture descriptor of the image on the basis of the feature values of the image.