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Proceedings ArticleDOI

An intelligent on-line system for content based image retrieval

TL;DR: The paper presents the extension of the CBIR system for multiple queries such as "many blue and few green" and a number of experiments using the online system are conducted and the results are included and discussed in the paper.
Abstract: We propose an intelligent content based image retrieval system and it is an extension of a previously published research paper (S. Kulkarni et al., 1999), where a neuro-fuzzy technique was presented. The CBIR system will accept multiple queries as input such as "mostly red and many blue and few green" that can be provided online and the outputs of the system are the images with their confidential values. The system uses fuzzy logic to interpret multiple natural expressions such as mostly, many and few and a neural network to learn the meaning of mostly red, many red and few red. The system has been implemented on the World Wide Web using CGI scripts and C programming language. Previously (S. Kulkarni et al., 1999), we presented some preliminary results using a single query. The paper presents the extension of our online system for multiple queries such as "many blue and few green". A number of experiments using our online system is conducted and the results are included and discussed in the paper.
Citations
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Journal ArticleDOI

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TL;DR: The aim of this research is to highlight the efforts of researchers who have conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.
Abstract: In the current era of digital communication, the use of digital images has increased for expressing, sharing andinterpreting information. While working with digital images, quite often it is necessary to search for a specific image for aparticular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of imagesbut it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same contentbasedsearching task becomes extremely complex when the number of images is in the millions. To deal with thesituation, some intelligent way of content-based searching is required to fulfill the searching request with right visualcontents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficientand robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers whoconducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.

37 citations


Cites background from "An intelligent on-line system for c..."

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01 Jan 2009

8 citations

Proceedings ArticleDOI

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25 Jun 2010
TL;DR: A content-based parallel image retrieval system to achieve high responding ability, developed on cluster architectures, that uses the Symmetrical Color-Spatial Features (SCSF) to represent the content of an image.
Abstract: As we all know, the content-based image retrieval (CBIR) is very time-consuming due to the extraction and matching of high dimensional and complex features. The traditional CBIR systems could not respond to a very large number of retrieval requests at mean time, which are submitted from the Internet. In this paper, we propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval servers to supply the service of content-based image retrieval. Our system adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. Our system uses the Symmetrical Color-Spatial Features (SCSF)[25] to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree[16], which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. (Abstract)

7 citations

Book ChapterDOI

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12 Mar 2000
TL;DR: In this paper, an image is segmented into "homogeneous" regions using a histogram clustering algorithm and each image is then represented by a set of regions with region descriptors.
Abstract: Representing general images using global features extracted from the entire image may be inappropriate because the images often contain several objects or regions that are totally different from each other in terms of visual image properties. These features cannot adequately represent the variations and hence fail to describe the image content correctly. We advocate the use of features extracted from image regions and represent the images by a set of regional features. In our work, an image is segmented into "homogeneous" regions using a histogram clustering algorithm. Each image is then represented by a set of regions with region descriptors. Region descriptors consist of feature vectors representing color, texture, area and location of regions. Image similarity is measured by a newly proposed Region Match Distance metric for comparing images by region similarity. Comparison of image retrieval using global and regional features is presented and the advantage of using regional representation is demonstrated.

7 citations

Proceedings ArticleDOI

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22 Jul 2009
TL;DR: In this paper, a color and texture based neural network -fuzzy logic approach for content based image retrieval using 2D-wavelet transform was proposed, which improved the retrieval performance by learning and searching capability of the neural network combined with the fuzzy interpretation.
Abstract: In this paper we introduce the new integrated color and texture based image retrieval technique using neuro-fuzzy approach for content based image retrieval. Most of the image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we developed color and texture based neural network -fuzzy logic approach for content based image retrieval using 2D-wavelet transform. The system performance improved by the learning and searching capability of the neural network combined with the fuzzy interpretation. This overcomes the vagueness and inconsistency due to human subjectivity. Multiresolution analysis using 2D-DWT can decompose the image into components at different scales, so that the coarest scale components carry the global approximation information while the finer scale components contain the detailed information. The empirical results show that the precision improved from 67% to 98% and average recall rate of 67% to 98% for the general purpose database size of 10000 images compared with existing approaches.

1 citations

References
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Journal ArticleDOI

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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. >

3,922 citations

Proceedings ArticleDOI

[...]

26 Oct 1997
TL;DR: An implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database, is presented.
Abstract: We present an implementation of NeTra, a prototype image retrieval system that uses color texture, shape and spatial location information in segmented image database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object or region based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as "retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper one-third of the image" where the individual objects could be regions belonging to different images.

883 citations

Journal ArticleDOI

[...]

TL;DR: Results of tests with the new color-constant-color-indexing algorithm show that it works very well even when the illumination varies spatially in its intensity and color, which circumvents the need for color constancy preprocessing.
Abstract: Objects can be recognized on the basis of their color alone by color indexing, a technique developed by Swain-Ballard (1991) which involves matching color-space histograms. Color indexing fails, however, when the incident illumination varies either spatially or spectrally. Although this limitation might be overcome by preprocessing with a color constancy algorithm, we instead propose histogramming color ratios. Since the ratios of color RGB triples from neighboring locations are relatively insensitive to changes in the incident illumination, this circumvents the need for color constancy preprocessing. Results of tests with the new color-constant-color-indexing algorithm on synthetic and real images show that it works very well even when the illumination varies spatially in its intensity and color. >

667 citations


"An intelligent on-line system for c..." refers background in this paper

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Journal ArticleDOI

[...]

TL;DR: An implementation of NeTra, a prototype image retrieval system that uses color texture, shape and spatial location information in segmented image database that incorporates a robust automated image segmentation algorithm that allows object or region based search.
Abstract: We present here an implementation of NeTra, a prototype image retrieval system that uses color, texture, shape and spatial location information in segmented image regions to search and retrieve similar regions from the database. A distinguishing aspect of this system is its incorporation of a robust automated image segmentation algorithm that allows object- or region-based search. Image segmentation significantly improves the quality of image retrieval when images contain multiple complex objects. Images are segmented into homogeneous regions at the time, of ingest into the database, and image attributes that represent each of these regions are computed. In addition to image segmentation, other important components of the system include an efficient color representation, and indexing of color, texture, and shape features for fast search and retrieval. This representation allows the user to compose interesting queries such as "retrieve all images that contain regions that have the color of object A, texture of object B, shape of object C, and lie in the upper of the image", where the individual objects could be regions belonging to different images. A Java-based web implementation of NeTra is available at http://vivaldi.ece.ucsb.edu/Netra.

619 citations


"An intelligent on-line system for c..." refers background in this paper

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Proceedings ArticleDOI

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23 Oct 1995
TL;DR: This approach identifies the regions within images that contain colors from predetermined color sets by searching over a large number of color sets, which allows very fast indexing of the image collection by the color contents of the images.
Abstract: We propose a method for automatic color extraction and indexing to support color queries of image and video databases. This approach identifies the regions within images that contain colors from predetermined color sets. By searching over a large number of color sets, a color index for the database is created in a fashion similar to that for file inversion. This allows very fast indexing of the image collection by the color contents of the images. Furthermore, information about the identified regions, such as the color set, size, and location, enables a rich variety of queries that specify both color content and spatial relationships of regions. We present the single color extraction and indexing method and contrast it to other color approaches. We examine single and multiple color extraction and image query on a database of 3000 color images.

215 citations


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