Content based image retrieval using mobile agents
23 Sep 1999-pp 248-252
TL;DR: A generic architecture for content based retrieval of images, which can be extended to the requirements of large distributed and heterogeneous collections, and is modeled as a multi agent system where an autonomous search agent encapsulates independent image retrieval algorithms.
Abstract: We present a generic architecture for content based retrieval of images, which can be extended to the requirements of large distributed and heterogeneous collections. The system is modeled as a multi agent system where an autonomous search agent encapsulates independent image retrieval algorithms. An optimal team of agents is dynamically selected for every retrieval problem. A flexible protocol allows for dynamic addition of search agents incorporating new pattern recognition techniques. These agents are coded as mobile agents, so that they can travel across a wide area network and analyze the documents at their sources. The separation of physical image forms and their logical structural composition allows the search agents to operate over heterogeneous repositories. A prototype implementation validates the effectiveness of the architecture.
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13 Mar 2005
TL;DR: This work presents the prototype of the secure and distributed search engine, which dynamically pushes content based feature extraction to image providers, and gives a description of the search engine's architecture and implementation, quantitative evaluation results, and a discussion of related security mechanism for content protection and server security.
Abstract: Current search engines crawl the Web, download content, and digest this content locally. For multimedia content, this involves considerable volumes of data. Furthermore, this process covers only publicly available content because content providers are concerned that they otherwise loose control over the distribution of their intellectual property. We present the prototype of our secure and distributed search engine, which dynamically pushes content based feature extraction to image providers. Thereby, the volume of data that is transported over the network is significantly reduced, and the concerns mentioned above are alleviated. The distribution of feature extraction and matching algorithms is done by mobile software agents. We give a description of the search engine's architecture and implementation, quantitative evaluation results, and a discussion of related security mechanism for content protection and server security.
17 citations
Cites background from "Content based image retrieval using..."
...The idea of using mobile agents for content based image retrieval has been mentioned before [6, 5, 7]....
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...The basic concepts of mobile agent based image search engines have been mentioned by several authors before [4, 5, 6, 7] but have not yet been addressed in sufficient detail and in the context of a...
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TL;DR: This work presents the prototype of a secure and distributed search engine, which dynamically pushes content based feature extraction to image providers, and describes the search engine's architecture and implementation, and depicts the concepts to integrate agent and Web service technology.
Abstract: Current search engines crawl the Web, download content, and digest this
content locally. For multimedia content, this involves considerable volumes
of data. Furthermore, this process covers only publicly available content
because content providers are concerned that they otherwise loose control
over the distribution of their intellectual property. We present the
prototype of our secure and distributed search engine, which dynamically
pushes content based feature extraction to image providers. Thereby, the
volume of data that is transported over the network is significantly
reduced, and the concerns mentioned above are alleviated. The distribution
of feature extraction and matching algorithms is done by mobile software
agents. Subsequent search requests performed upon the resulting feature
indices by means of remote feature comparison can either be realized through
mobile software agents, or by the use of implicitly created Web services
which wrap the remote comparison functionality, and thereby improve the
interoperability of the search engine. We give a description of the search
engine's architecture and implementation, depict our concepts to integrate
agent and Web service technology, and present quantitative evaluation
results. Furthermore, we discuss related security mechanisms for content
protection and server security.
9 citations
Cites background from "Content based image retrieval using..."
...The basic concepts of mobile agent based image search engines have been mentioned by several authors before [6, 2, 39, 54] but have not yet been addressed in sufficient detail and in the context of a practical system....
[...]
...The idea of using mobile agents for content based image retrieval has been mentioned before [39, 2, 54]....
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References
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TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
14,562 citations
"Content based image retrieval using..." refers background in this paper
...There are two special agents, one specializes in recognition of faces of some personalities using eigen space matching [7] and the other uses local feature matching and can be optimized for identi cation of a particular logo or a ag....
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TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.
Abstract: Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks.
This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.
5,672 citations
IBM1
TL;DR: The Query by Image Content (QBIC) system as discussed by the authors allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information.
Abstract: Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >
3,957 citations
17 Jun 1997
TL;DR: Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
Abstract: We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
1,956 citations
"Content based image retrieval using..." refers methods in this paper
...They implement standard algorithms, for example, using colour histograms[8], edge detection(Laplacian)[5], chromaticity, correlogram property [6] and gabor(texture) [9]....
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Proceedings Article•
30 May 1997
TL;DR: The Query by Image Content (QBIC) system as mentioned in this paper allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information.
Abstract: Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. QBIC allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. Two key properties of QBIC are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. This article describes the QBIC system and demonstrates its query capabilities. QBIC technology is part of several IBM products. >
1,597 citations