Combining structure, color and texture for image retrieval: A performance evaluation

Qasim, Iqbal; Aggarwal, J. K.

doi:10.1109/icpr.2002.1048333

Cited by 43 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The combination of multiple features has received much attention in recent years. Iqbal and Aggarwal [2002] combine linguistic color labels and a Gabor texture index for image-indexing. Rui et al [1998] combine color, texture, and shape information and use the user's feedback about the relevance of the search results to update the weighting of the histograms.…”

Section: Introductionmentioning

confidence: 99%

Image retrieval and perceptual similarity

Neumann

Gegenfurtner

2006

ACM Trans. Appl. Percept.

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Simple, low-level visual features are extensively used for content-based image retrieval. Our goal was to evaluate an imageindexing system based on some of the known properties of the early stages of human vision. We quantitatively measured the relationship between the similarity order induced by the indexes and perceived similarity. In contrast to previous evaluation approaches, we objectively measured similarity both for the few best-matching images and also for relatively distinct images. The results show that, to a large degree, the rank orders induced by the indexes predict the perceived similarity between images. The highest index concordance employing a single index was obtained using the chromaticity histogram. Combining different information sources substantially improved the correspondence with the observers. We conclude that image-indexing systems can provide useful measures for perceptual image similarity. The methods presented here can be used to evaluate and compare different image-retrieval systems.

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Section: Introductionmentioning

confidence: 99%

Image retrieval and perceptual similarity

Neumann

Gegenfurtner

2006

ACM Trans. Appl. Percept.

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“…Pre-processing operations such as Gaussian or linear feature normalizations [29,30], the assignment of different weights to each set of descriptors [29,31,32], and the use of the product and sum rules are common in this context. Nevertheless, a much more appropriate option is to incorporate subjectivity into the model by using previously gathered user preferences.…”

Section: Probability Of Relevance From User Datamentioning

confidence: 99%

A naive relevance feedback model for content-based image retrieval using multiple similarity measures

Arevalillo‐Herráez

Ferri

Domingo

2010

Pattern Recognition

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“…Such a grouping can serve as a spatial layout or as a rough sketch by additional postprocessing. It has been successfully used in nonmedical domain by the CIRES (http://amazon.ece.utexas.edu/∼qasim/) system, 26 which is based upon a combination of higher-level and lower-level computer vision principles. While the term lower-level refers to basic image features (color and/or texture, see above), higher-level analysis benefits from perceptual organization, inference and grouping principles to extract information describing the structural content of an image.…”

Section: Image Features/descriptorsmentioning

confidence: 99%

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

et al. 2010

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Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

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Combining structure, color and texture for image retrieval: A performance evaluation

Cited by 43 publications

References 10 publications

Image retrieval and perceptual similarity

Image retrieval and perceptual similarity

A naive relevance feedback model for content-based image retrieval using multiple similarity measures

Content-Based Image Retrieval in Radiology: Current Status and Future Directions

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