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Analyzing Scenery Images by Monotonic Tree
- ACM Multimedia Systems Journal
, 2002
"... Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Research ..."
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Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of techniques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Research efforts are needed to bridge the gap between high-level semantics and low-level features. In this paper, we present a novel approach to supporting semantics-based image retrieval. Our approach is based on the monotonic tree, a derivation of the contour tree for use with discrete data. The structural elements of an image are modeled as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such properties as color, spatial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme is applied to the analysis and retrieval of scenery images. Comparisons of experimental results of this approach with conventional techniques using low-level features, demonstrate the effectiveness of our approach. Keywords: Content-based image retrieval, image feature extraction, annotation, semantics retrieval, monotonic tree 1
Classifying offensive sites based on image content
- In Computer Vision and Image Understanding
, 2004
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On video retrieval: content analysis by ImageMiner™
- Proc. SPIE: Storage and Retrieval for Image and Video Databases
, 1998
"... In this paper videos are analyzed to get a content-based decription of the video. The structure of a given video is useful to index long videos efficiently and automatically. A comparison between shots gives an overview about cut frequency, cut pattern, and scene bounds. After a shot detection the s ..."
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In this paper videos are analyzed to get a content-based decription of the video. The structure of a given video is useful to index long videos efficiently and automatically. A comparison between shots gives an overview about cut frequency, cut pattern, and scene bounds. After a shot detection the shots are grouped into clusters based on their visual similarity. A time-constraint clustering procedure is used to compare only those shots that are positioned inside a time range. Shots from different areas of the video (e.g., begin/end) are not compared. With this cluster information that contains a list about shots and their clusters it is possible to calculate scene bounds. A labeling of all clusters gives a declaration about the cut pattern. It is easy now to distinguish a dialogue from an action scene. The final content analysis is done by the ImageMiner system. The ImageMiner system developed at the University
PICSearch - A Platform for Image Content-based Searching Algorithms
- in Proceedings of the Sixth Int. Conf. in Central Europe on Computer Graphics and Visualization (WSCG'98
, 1998
"... An environment for content-based pictorial retrieval algorithms, called PICSearch, is introduced. In this context, the retrieval is based on color distribution, texture or edges of a query image, or a sketch; i.e. the boundary information of a shape. PICSearch is designed to serve as a platform for ..."
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Cited by 4 (1 self)
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An environment for content-based pictorial retrieval algorithms, called PICSearch, is introduced. In this context, the retrieval is based on color distribution, texture or edges of a query image, or a sketch; i.e. the boundary information of a shape. PICSearch is designed to serve as a platform for any kind of pictorial matching algorithm. The system is designed for researchers developing such algorithms. PICSearch provides an easy-to-use graphical interface and a platform, where the researcher can easily embed his algorithm without the need to create a whole system from scratch. PICSearch is very independent on the underlying operating system and window manager. PICSearch is released to public use (under the GNU General Public License) and, to our knowledge, it is the first open platform to image retrieval systems freely available. Keywords: content-based image retrieval, visual information management, open platforms, image databases Computing Reviews (1991) Categories: H.3.3 [Inf....
Sceneryanalyzer: A System Supporting Semantics-Based Image Retrieval
"... Introduction With the advance of multimedia technology, image data in various formats are becoming available at an explosive rate. With such enormous data resources, the search and retrieval of image databases are demanded to provide open access to relevant information and products. Thus, content-b ..."
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Introduction With the advance of multimedia technology, image data in various formats are becoming available at an explosive rate. With such enormous data resources, the search and retrieval of image databases are demanded to provide open access to relevant information and products. Thus, content-based image retrieval (CBIR) has become an active research area. A variety of techniques have been developed. In particular, content-based image retrieval using lowlevel features such as color [41, 36, 26], texture [21, 35, 34, 40, 20], shape [42, 22, 14, 23, 24, 15, 10, 17, 46, 47, 33, 43, 32] and others [30, 38, 2, 16, 7] extracted from the images has been well studied. Various image querying systems including QBIC [11], VisualSeek [36], PhotoBook [27] and Virage [5] have been built based on the low-level features for general or specific image retrieval tasks. However, retrieving images based on low-level features has been proven unsatisfactory. Thus, effective and precise image retrieval
An intelligent content-based image retrieval system based on color, shape and spatial relations
- In Proceedings of Natl. Sci. Counc. ROC(A
, 2001
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© Springer-Verlag 2003 Analyzing scenery images by monotonic tree
"... Abstract. Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of tech-niques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level querie ..."
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Abstract. Content-based image retrieval (CBIR) has been an active research area in the last ten years, and a variety of tech-niques have been developed. However, retrieving images on the basis of low-level features has proven unsatisfactory, and new techniques are needed to support high-level queries. Re-search efforts are needed to bridge the gap between high-level semantics and low-level features. In this paper, we present a novel approach to support semantics-based image retrieval. Our approach is based on the monotonic tree, a derivation of the contour tree for use with discrete data. The structural el-ements of an image are modeled as branches (or subtrees) of the monotonic tree. These structural elements are classified and clustered on the basis of such properties as color, spa-tial location, harshness and shape. Each cluster corresponds to some semantic feature. This scheme is applied to the anal-ysis and retrieval of scenery images. Comparisons of experi-mental results of this approach with conventional techniques using low-level features demonstrate the effectiveness of our approach.
based on
"... Content-based multimedia information retrieval is an interesting but difficult research. Current approaches include the use of color, texture, and shape information. In this paper, we present a hybrid approach, which incorporates color, shape and spatial relations among objects in a picture, to retr ..."
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Content-based multimedia information retrieval is an interesting but difficult research. Current approaches include the use of color, texture, and shape information. In this paper, we present a hybrid approach, which incorporates color, shape and spatial relations among objects in a picture, to retrieve images from nearly 5000 pictures. A revised color scheme and its indexing technique are used to improve the efficiency of retrieval, according to our clustering method and color sensation. A seed filling like mechanism is used to extract shape and spatial relations among objects. Qualitative approach is applied to the similarity comparison of spatial differences. The system also implemented with a friendly GUI, which allows sketch images, as well as relevance feed back to improve the accuracy of retrieval. Our experience shows that the system is able to retrieve image information efficiently by the proposed approaches.
