Image retrieval using both color and texture features

Shi, Dongcheng; Xu, Lan; Han, Lingyan

doi:10.1016/s1005-8885(08)60020-5

Cited by 22 publications

(10 citation statements)

References 5 publications

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“…The extracted features can be grouped into the following types: (a) color representation, (b) texture representation, (c) local features, and (d) shape representation [15]. The features which are presented in this paper are grouped according to two categories which are color and texture representation.…”

Section: Feature Extraction Techniquementioning

confidence: 99%

A novel image retrieval model based on the most relevant features

ElAlami

2011

Knowledge-Based Systems

154

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Section: Feature Extraction Techniquementioning

confidence: 99%

A novel image retrieval model based on the most relevant features

ElAlami

2011

Knowledge-Based Systems

154

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“…Thus, some works in the literature present studies regarding the best configuration between a given distance function and a type of feature [4], [13], [1] in order to optimize the retrieval effectiveness [17].…”

Section: Distance Functionsmentioning

confidence: 99%

Content-based image retrieval towards the automatic characterization of soybean seed vigor

Santanna

Saito

Bugatti

2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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One of the main issues related to the treatment of soybean seeds concerns its vigor definition. The great majority of vigor analysis is performed by a human specialist leading to an extremely tiresome and subjective approach, highly susceptible to failures. In order to deal with this problem the present paper proposed a methodology joining content-based image retrieval techniques with the specialist perception to retrieve soybean seeds according to his/her expectation and aiding the seed vigor definition. The experiments performed showed that the proposed approach presented several notable contributions to the soybean vigor definition process, reaching up to 100% of precision regarding the similarity queries. Moreover, it not only improves in a great extent the entire process from planting to harvesting, but also enables to automate and accelerate it.

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“…Several CBIR Smeulders et al (2000); Vailaya et al (2001) schemes were developed for indexing and retrieving images from database based on the significant features like colour Shrivastava and Tyagi (2014), texture Shi et al (2007) and shape Krishnamoorthy and Devi (2013); Chahooki and Charkari (2012). The straightforward image to image searching mechanism is not considered in CBIR.…”

Section: Introductionmentioning

confidence: 99%

Content-based image retrieval using SVD-based Eigen images

Varish

Pal

2016

IJIM

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In this paper, a content-based image retrieval scheme using the singular value decomposition (SVD) is proposed where the feature vector was estimated from the selected significant components of a singular value decomposed image. The Eigen values of the decomposed image are divided into several numbers of groups and from each group we have constructed several Eigen images and subsequently, statistical values like mean, standard deviation and entropy are computed from those Eigen images. The constructed Eigen images are suitable to analyse the original image data in various image planes. This approach is applied to each colour components for formation of colourbased final feature vector. This approach is appropriate to reduce the overall processing cost in image retrieving process due to the consideration of significant image feature in SVD domain. The scheme is tested on a standard Corel image database and satisfactoryresults are achieved.

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Image retrieval using both color and texture features

Cited by 22 publications

References 5 publications

A novel image retrieval model based on the most relevant features

A novel image retrieval model based on the most relevant features

Content-based image retrieval towards the automatic characterization of soybean seed vigor

Content-based image retrieval using SVD-based Eigen images

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