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Conference on storage and retrieval for image and video databases II

作者： NISHIMURA, R ODA, M NONAKA, S IMAIDE, T HITACHI LTD IMAGE & MEDIA SYST LABTOTSUKA KUYOKOHAMA 244JAPAN

ISBN: (纸本)0819414808

This paper describes a novel system for real-time object extraction from a moving video image. The object extraction method employed in this system has two features. The first one is multi-channel thresholding in a color space for extracting a target with several colors as a single object. The color space is normalized by luminance to make the process robust against light intensity fluctuations. The other feature is a key algorithm, called sequential growing, which exploits interfield correlation of a video image and realizes field-rate operation. In this algorithm, a binary image representing a target object is generated by growing an initially given mask image horizontally and vertically until whole object is extracted. We have also fabricated a 0.8 micrometers CMOS chip to incorporate the system into a compact video camera, which has successfully extracted objects in a number of general scenes.

关键词： video Cameras Detection and tracking algorithms Imaging systems Binary data image processing Signal processing Digital signal processing Feature extraction Seaborgium

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Multiscale branch and bound image database search 5

Multiscale branch and bound image database search

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Conference on storage and retrieval for image and video databases V

作者： Chen, JY Bouman, CA Allebach, JP Purdue University (United States)

ISBN: (纸本)0819424331

This paper presents a formal framework for designing search algorithms which can identify target images by the spatial distribution of color, edge and texture attributes. The framework is based on a multiscale representation of both the image data, and the associated parameter space that must be searched. We define a general form for the distance function which insures that branch and bound search can be used to find the globally optimal match. Our distance function depends on the choice of a convex measure of feature distance. For this purpose, we propose the L(1) norm and some other alternative choices such as the Kullback-Liebler and divergence distances. Experimental results indicate that the multiscale approach can improve search performance with minimal computational cost.

关键词： multiscale search image similarity content-based retrieval color histogram convex function

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Fast edge map extraction from MPEG compressed video data for video parsing

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3656卷 710-721页

作者： Song, Byung Cheol Ra, Jong Beom Korea Advanced Inst of Science and Technology Taejon Korea Republic of

For the last few years, shot boundary detection has been recognized as an important research issue on video retrieval. Also as a preliminary step for the task, it is essential to extract salient features from videos. Recently, it has become common to perform the two tasks in compressed domain to alleviate their computational costs. In this paper, we propose novel shot boundary detection technique, which uses two feature images, or DC and edge images, extracted directly from MPEG compressed video. While a DC image can be easily obtained, edge image extraction usually requires considerable computational burden. For fast edge image extraction, we suggest to utilize only a few AC coefficients of each DCT block in motion compensated P-frames and B-frames as well as I-frames. This drastically reduces the computational burden compared to edge extraction in the spatial domain. In order to further reduce the computational burden, another edge image extraction technique is also suggested on the basis of AC prediction using DC images. By using the edge energy diagram obtained from edge images and histograms from DC images, shot boundaries such as abrupt transitions, fades, and dissolves are detected automatically. Simulation results show that the proposed techniques are fast and effective.

关键词： Feature extraction

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Combining indexing and learning in iterative refinement

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3656卷 390-400页

作者： Li, Chung-Sheng Castelli, Vittorio Smith, John R. Bergman, Lawrence IBM T.J. Watson Research Cent Yorktown Heights United States

A simultaneous learning and indexing technique is proposed for efficient content-based retrieval of images that can be described by feature vectors. This technique builds a compact high-dimensional index while taking into account that the raw feature space needs to be adjusted for each new application. With this technique, much better efficiency can be achieved as compared to those techniques that do not make provisions for efficient indexing.

关键词： image enhancement

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Hierarchical clustering algorithm for fast image retrieval

Hierarchical clustering algorithm for fast image retrieval

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7th storage and retrieval for image and video databases Conference

作者： Krishnamachari, S Abdel-Mottaleb, M Philips Res Labs Briarcliff Manor NY 10510 USA

ISBN: (纸本)0819431273

image retrieval systems that compare the query image exhaustively with each individual image in the database are not scalable to large databases. A scalable search system should ensure that the search time does not increase linearly with the number of images in the database. We present a clustering based indexing technique, where the images in the database are grouped into clusters of images with similar color content using a hierarchical clustering algorithm. At search time the query image is not compared with all the images in the database, but only with a small subset. Experiments show that this clustering based approach offers a superior response time with a high retrieval accuracy. Experiments with different database sizes indicate that for a given retrieval accuracy the search time does not increase linearly with the database size.

关键词： image retrieval color histogram scalable clustering hierarchical

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Rotation, translation, and scaling invariant color image indexing

Rotation, translation, and scaling invariant color image ind...

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7th storage and retrieval for image and video databases Conference

作者： Celenk, M Shao, Y Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

ISBN: (纸本)0819431273

This paper describes a rotation, translation, and scaling (RTS) invariant color image indexing technique for imaging database systems. The features used for image indexing are color based, which are extracted using the principal component analysis, Hotelling transform, and moment invariants. This synthesized feature extraction technique is devised to be computationally efficient for on-line fast image storage and retrieval using color information Since the database indexing relies on a the use of average (mean) color vector and seven moment invariants of an image, the index storage requirement of the method is only a ten-dimensional (10-D) vector. This index storage efficiency is very desirable for many imaging database applications. A new similarity measure is also proposed based on the Tanimoto measure of recognizing similar patterns for fast image retrieval in large database systems. The underlying similarity measure is computationally effective since the vector inner product is the only operation needed for its computation. Four databases are experimented in the computer simulation of the algorithm to demonstrate the RTS property of the image retrieval. It is determined experimentally that the proposed method is not affected by substantial changes in the database images due to rotation, translation, and scaling. This is attributed to the fact that the moment features used for retrieval are not predefined sets;rather, they are obtained directly from the images submitted for recording or searching. This makes the algorithm very robust and attractive for many applications of the image storage and retrieval systems.

关键词： color image storage and retrieval principal components Hotelling transform moment invariants Tanimoto measure

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New anchor selection methods for image retrieval

New anchor selection methods for image retrieval

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Conference on storage and retrieval for Media databases 2003

作者： Natsev, A Smith, JR IBM Corp TJ Watson Res Ctr Hawthorne NY 10532 USA

ISBN: (纸本)0819448214

Anchoring is a technique for representing objects by their distances to a few well chosen landmarks, or anchors. Objects are mapped to distance-based feature vectors, which can be used for content-based retrieval, classification, clustering, and relevance feedback of images, audio, and video. The anchoring transformation typically reduces dimensionality and replaces expensive similarity computations in the original domain with simple distance computations in the anchored feature domain, while guaranteeing lack of false dismissals. Anchoring is therefore surprisingly simple, yet effective, and flavors of it have seen application in speech recognition, audio classification, protein homology detection, and shape matching. In this paper, we describe the anchoring technique in some detail and study methods for anchor selection, both from an analytical, as well as empirical, standpoint. Most work to date has largely ignored this problem by fixing the anchors to be the entire set of objects or by using greedy selection from among the set of objects. We generalize previous work by considering anchors from outside of the object space, and by deriving an analytical upper bound on the distance-approximation error of the method.

关键词： image retrieval databases Error analysis Feature extraction Vector spaces video image classification Proteins Transform theory Visualization

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Segmentation based image retrieval

Segmentation based image retrieval

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storage and retrieval for image and video databases VI Conference

作者： Siebert, A Univ British Columbia Dept Comp Sci Vancouver BC V6T 1W5 Canada

ISBN: (纸本)0819427527

We have been developing an image retrieval system, called MIPS (Multiscalar image Processing and retrieval System), for use in uncontrolled environments. On insertion into the image database, the images are automatically segmented into homogeneous regions. Generic features are computed and stored for each segment. Specifically, we maintain not only geometric and photometric attributes but also simple spatial information for each extracted region. This approach asks the user to construct queries in terms of the given primitives, i.e. regions and their spatial relations. Preliminary results show that the success of the system depends on how well the images can be modelled by homogeneous regions, on how useful the generic features are for the given application, and on the knowledge that the user puts into the formulation of the queries. A fully automatic segmentation algorithm is of paramount importance. We have designed an algorithm called Perceptual Region Growing that combines region growing, edge detection, and perceptual organization principles, without resorting to any kind of high level knowledge or interactive user intervention. Decision thresholds and quality measures are directly derived from the image data, based on image statistics. Search through critical parameter spaces is the key idea to cope with noise in uncontrolled environments. The dynamics of the region growing process is constantly monitored and exploited.

关键词： image retrieval segmentation region growing

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Querying multiple perspective video

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3656卷 264-271页

作者： Santini, Simone Gupta, Amarnath Jain, Ramesh Univ of California San Diego United States

This paper introduces a model of spatio-temporal database that we are developing to query interesting events in video sequences. The database that we are designing is pushing the state of the art for a number of fields, and there are many issues that are still waiting a satisfactory solution. In this paper we present our (albeit still partial) answer to some of these problems, and the future directions of our work. Our design is divided in two layers: a Logbook which operates as a short time repository of unsummarized and unprocessed data, and a long term spatio-temporal database which stores and queries summarized data.

关键词： video signal processing

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video hypermedia authoring using automatic object tracking

Video hypermedia authoring using automatic object tracking

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storage and retrieval for image and video databases VI Conference

作者： Kanda, J Wakimoto, K Abe, H Tanaka, S Mitsubishi Elect Corp Informat Technol R&D Ctr Kanagawa 247 Japan

ISBN: (纸本)0819427527

video hypermedia systems enable users to retrieve information related to an object by selecting it directly in a video sequence. In video hypermedia systems, users must locate an anchor position according to the motion of the object. But it is very laborious to locate an anchor to its suitable position according to the motion of the object. We have proposed a new automatic object tracking method and implemented it to the system. A feature of this method is that it includes various automatic error correction algorithms. We evaluated this system on effectiveness on reducing human operations. As a result, the number of operations reduced to 30.3% of the former method, and the time of operations reduced to 60.1% of the former method.

关键词： hypermedia pattern recognition object tracking video database

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