CIRES: a system for content-based retrieval in digital image libraries

Qasim, Iqbal; Aggarwal, J. K.

doi:10.1109/icarcv.2002.1234821

Cited by 52 publications

(21 citation statements)

References 15 publications

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“…Color histograms are frequently used in CBIR systems [6], [17], [22], [40]. Histogram is useful though its global characterization is weak.…”

Section: B Colormentioning

confidence: 99%

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Content-Based Image Retrieval Using Texture Color Shape and Region

Shirazi¹,

Umar²,

Naz³

et al. 2016

ijacsa

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Abstract-Interests to accurately retrieve required images from databases of digital images are growing day by day. Images are represented by certain features to facilitate accurate retrieval of the required images. These features include Texture, Color, Shape and Region. It is a hot research area and researchers have developed many techniques to use these feature for accurate retrieval of required images from the databases. In this paper we present a literature survey of the Content Based Image Retrieval (CBIR) techniques based on Texture, Color, Shape and Region. We also review some of the state of the art tools developed for CBIR.

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“…Color histograms are frequently used in CBIR systems [6], [17], [22], [40]. Histogram is useful though its global characterization is weak.…”

Section: B Colormentioning

confidence: 99%

“…The orthogonal based sets are not formed by Gabor function that is why its representation is not solid and it consumes more computation cycles and more memory. In [17] a CBIR system is presented. It extract textural and color features of image.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval Using Texture Color Shape and Region

Shirazi¹,

Umar²,

Naz³

et al. 2016

ijacsa

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show abstract

“…This overview enables to confirm, as it was said before, that commonly designed CBIR systems are generally based on visual features such as colour, texture and shape. In (Iqbal & Aggarwal, 2002) is presented CIRES (Content-based Image REtrieval System), an online system for retrieval in image libraries. It is done to extend the retrieval paradigm, which was mostly limited to colour and texture analyses, by using image structure.…”

Section: An Overview Of the Advances In Pattern Recognitionmentioning

confidence: 99%

An Overview of Advances of Pattern Recognition Systems in Computer Vision

Kpalma¹,

Ronsin²

2007

Vision Systems: Segmentation and Pattern Recognition

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“…Photobook uses appearance features, texture features and 2D shape features. Other state-of-the-art CBIR systems include SIMBA, 45 CIRES, 22 SIMPLIcity, 49 IRMA, 29 FIRE, 8 MIRROR, 51 and also those in Refs. 21, 28 and 47.…”

Section: Introductionmentioning

confidence: 99%

Accurate Image Retrieval Based on Compact Composite Descriptors and Relevance Feedback Information

Chatzichristofis

Zagoris

Boutalis

et al. 2010

Int. J. Patt. Recogn. Artif. Intell.

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In this paper a new set of descriptors appropriate for image indexing and retrieval is proposed. The proposed descriptors address the tremendously increased need for e±cient content-based image retrieval (CBIR) in many application areas such as the Internet, biomedicine, commerce and education. These applications commonly store image information in large image databases where the image information cannot be accessed or used unless the database is organized to allow e±cient storage, browsing and retrieval. To be applicable in the design of large image databases, the proposed descriptors are compact, with the smallest requiring only 23 bytes per image. The proposed descriptors' structure combines color and texture information which are extracted using fuzzy approaches. To evaluate the performance of the proposed descriptors, the objective Average Normalized Modi¯ed Retrieval Rank (ANMRR) is used. Experiments conducted on¯ve benchmarking image databases demonstrate the e®ectiveness of the proposed descriptors in outperforming other state-of-the-art descriptors. Also, a Auto Relevance Feedback (ARF) technique is introduced which is based on the proposed descriptors. This technique readjusts the initial retrieval results based on user preferences improving the retrieval score signi¯cantly. An online demo of the image retrieval system img(Anaktisi) that implements the proposed descriptors can be found at http://www.anaktisi.net.

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CIRES: a system for content-based retrieval in digital image libraries

Cited by 52 publications

References 15 publications

Content-Based Image Retrieval Using Texture Color Shape and Region

Content-Based Image Retrieval Using Texture Color Shape and Region

An Overview of Advances of Pattern Recognition Systems in Computer Vision

Accurate Image Retrieval Based on Compact Composite Descriptors and Relevance Feedback Information

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