Modern computer applications use enormous volumes of rich data like video, still images, and text, as well as more conventional numeric and character data. Managing huge volumes of such diverse data requires a databas...
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ISBN:
(纸本)081941767X
Modern computer applications use enormous volumes of rich data like video, still images, and text, as well as more conventional numeric and character data. Managing huge volumes of such diverse data requires a database. Content queries, such as 'find me the color images with red components higher than this threshold,' require that the database system be able to apply the qualification directly. Relational database systems that store images as untyped binary large objects (BLOBS) cannot apply qualifications like this, because the database system does not understand the contents of the BLOB. Object-Relational Database Management Systems (ORDBMS), on the other hand, allow users to extend the set of types and functions known to the database system. Programmers can write code that is dynamically loaded into the database server, and that operates on complex data types such as images. Those functions can be used in standard SQL queries, and the database manager can use new types and function results in indices to support fast queries on complex data. In addition, the query optimizer can be told how expensive the new functions are, so that it chooses an optimal strategy for satisfying complicated queries with many different predicates in their qualifications.
The temporal and multi-modal nature of video increases the dimensionality of content based retrieval problem. This places new demands on the indexing and retrieval tools required. The Virage video Engine (VVE) with th...
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ISBN:
(纸本)0819424331
The temporal and multi-modal nature of video increases the dimensionality of content based retrieval problem. This places new demands on the indexing and retrieval tools required. The Virage video Engine (VVE) with the default set of primitives provide the necessary frame work and basic tools for video content based retrieval. The video engine is a flexible platform independent architecture which provides support for processing multiple synchronized data streams like image sequences, audio and closed captions. The architecture allows for multi-modal indexing and retrieval of video through the use of media specific primitives. This paper presents the use of the VVE framework for content based videoretrieval.
The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to sho...
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The detection of shot boundaries in video sequences is an important task for generating indexed videodatabases. This paper provides a comprehensive quantitative comparison of the metrics that have been applied to shot boundary detection. In addition, several standardized statistical tests that have not been applied to this problem, and three new metrics, are considered. A mathematical framework for quantitatively comparing metrics is supplied. Experimental results based on a video database containing 39,000 frames are included.
In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a goo...
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In this paper, we present an approach to clustering video sequences and images for efficient retrieval using relative entropy as our cost criterion. In addition, our experiments indicate that relative entropy is a good similarity measure for content-based retrieval. In our clustering work, we treat images and video as probability density functions over the extracted features. This leads us to formulate a general algorithm for clustering densities. In this context, it can be seen that an euclidean distance between features and the Kullback-Liebler (KL) divergence give equivalent clustering. In addition, the asymmetry of the KL divergence leads to another clustering. Our experiments indicate that this clustering is more robust to noise and distortions compared with the one resulting from euclidean norm.
Current feature-based imagedatabases can typically perform efficient and effective searches on scalar feature information. However, many important features, such as graphs, histograms, and probability density functio...
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ISBN:
(纸本)081941767X
Current feature-based imagedatabases can typically perform efficient and effective searches on scalar feature information. However, many important features, such as graphs, histograms, and probability density functions, have more complex structure. Mechanisms to manipulate complex feature data are not currently well understood and must be further developed. The work we discuss in this paper explores techniques for the exploitation of spectral distribution information in a feature-based image database. A six band image was segmented into regions and spectral information for each region was maintained. A similarity measure for the spectral information is proposed and experiments are conducted to test its effectiveness. The objective of our current work is to determine if these techniques are effective and efficient at managing this type of image feature data.
This article presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Since shape is one of the most widely used image feature exploited in con...
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This article presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Since shape is one of the most widely used image feature exploited in content-based imageretrieval systems, the authors studied for each descriptor, the number of coefficients needed for indexing and their retrieval performance. Specifically, the authors studied Fourier, curvature scale space, angular radial transform (ART) and image moment descriptors for shape representation. The four shape descriptors are evaluated against each other using the standard methodology and the two most appropriate and available databases. The results showed that moment descriptors present the best performance in terms of shape representation quality while ART presents the lowest descriptor size.
A new system, the so-called MUVIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUVIS allows indexing of objects and i...
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A new system, the so-called MUVIS, is introduced for content-based indexing and retrieval for image database management systems. In addition to traditional indexing by key words, MUVIS allows indexing of objects and images based on color, texture, shape and objects layout inside them. Due to the use of large vector features, the pyramid trees are employed to create the index structure.
The development of increasingly complex multimedia applications calls for new methodologies for the organization and retrieval of still images and video sequences. Query and retrieval methods based on image content pr...
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ISBN:
(纸本)0819424331
The development of increasingly complex multimedia applications calls for new methodologies for the organization and retrieval of still images and video sequences. Query and retrieval methods based on image content promise good results, are currently widely investigated and, to some extent, already commercially available. Yet a large number of issues remain unsolved. In this paper we describe some results of a study on similarity evaluation in imageretrieval using color, object orientation and relative position as content features. A simple prototype system is also introduced that computes the feature descriptors and performs queries. Although not trivial, the features extraction process is completely automated and requires no user intervention. The system is admittedly not a general purpose tool, but is oriented to thematic image repositories where the semantics of stored images are limited to a specific domain.
Similarity between images is used for storage and retrieval in imagedatabases. In the literature, several similarity measures have been proposed that may be broadly categorized as: (1) metric based, (2) set-theoretic...
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ISBN:
(纸本)081941767X
Similarity between images is used for storage and retrieval in imagedatabases. In the literature, several similarity measures have been proposed that may be broadly categorized as: (1) metric based, (2) set-theoretic based, and (3) decision-theoretic based measures. In each category, measured based on crisp logic as well as fuzzy logic are available. In some applications such as imagedatabases, measures based on fuzzy logic would appear to be naturally better suited, although so far no comprehensive experimental study has been undertaken. In this paper, we report results of some of the experiments designed to compare various similarity measures for application to imagedatabases. We are currently working with texture images and intend to work with face images in the near future. As a first step for comparison, the similarity matrices for each of the similarity measures are computed over a set of selected textures and are presented as visual images. Comparative analysis of these images reveals the relative characteristics of each of these measures. Further experiments are needed to study their sensitivity to small changes in images such as illumination, magnification, orientation, etc. We describe these experiments (sensitivity analysis, transition analysis, etc.) that are currently in progress. The results from these experiments offer assistance in choosing the appropriate measure for applications to imagedatabases.
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