A scheme of colour image retrieval from databases

Fuertes, José Manuel; Lucena, Manuel; Blanca, Nicolás Pérez de la; Chamorro-Martı́nez, J.

doi:10.1016/s0167-8655(00)00128-8

Cited by 26 publications

(10 citation statements)

References 45 publications

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“…The color feature has widely been used in CBIR systems, because of its easy and fast computation [5] [6]. Color is also an intuitive feature and plays an important role in image matching.…”

Section: Color Featurementioning

confidence: 99%

A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

Girgis¹,

Reda²

2014

IJCA

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Color spaces, color histograms, histogram distance measurements, size and quantization play an important role in retrieving images based on similarities. This paper presents a study of the effect of color quantization schemes for different color spaces (HSV, YIQ and YCbCr) on the performance of content-based image retrieval (CBIR), using different histogram distance measurements (Histogram Euclidean Distance and Histogram Intersection Distance). For the purpose of this study, a CBIR system that implements two content-based image retrieval algorithms has been developed. The first algorithm is based only on the color feature, while the second one is based on combination of the color and texture features. The color histogram is used for image color feature extraction and Haar wavelet transform is used for image texture feature extraction. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments of this study. The experimental results show which histogram distance measurement is best, which color space gives better retrieval precision, and the best quantization schemes for the considered color spaces, when using only the color feature, and when using a combination of the color and texture features. General TermsContent-Based Image Retrieval, Image Processing.

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“…The color feature has widely been used in CBIR systems, because of its easy and fast computation [5] [6]. Color is also an intuitive feature and plays an important role in image matching.…”

Section: Color Featurementioning

confidence: 99%

A Study of the Effect of Color Quantization Schemes for Different Color Spaces on Content-based Image Retrieval

Girgis¹,

Reda²

2014

IJCA

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show abstract

“…The color feature has widely been used in CBIR systems, because of its easy and fast computation [10] [11]. Color is also an intuitive feature and plays an important role in image matching.…”

Section: Color Featurementioning

confidence: 99%

Content-based Image Retrieval using Image Partitioning with Color Histogram and Wavelet-based Color Histogram of the Image

Girgis¹,

Reda²

2014

IJCA

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This paper presents two content-based image retrieval algorithms that are based on image partitioning. The retrieval in the first algorithm is based only on the image color feature represented by the color histogram, while the retrieval in the second one is based on the image color and texture features represented by the color histogram and Haar wavelet transform, respectively. In these algorithms, each image in the database and the query image are divided into 4-equal sized blocks. Color and texture features are extracted for each block. Distances between the blocks of the query image and the blocks of a database image are calculated, then, the similarity between the query image and the database image is calculated by finding the minimum cost matching based on most similar highest priority (MSHP) principle. A CBIR system that implements the proposed algorithms has been developed. To evaluate the effectiveness of the proposed algorithms, experiments have been carried out using different color quantization schemes for three different color spaces (HSV, YIQ and YCbCr) with two similarity measures, namely the Histogram Euclidean Distance and Histogram Intersection Distance. The WANG image database, which contains 1000 general-purpose color images, has been used in the experiments. General TermsContent-Based Image Retrieval, Image Processing.

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“…QBE image retrieval method has numerous advantages for many practical applications [1,2,5,7,11,15]. For instance, trademarks are specially designed marks used to identify companies, products or services.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers deter-0262-8856/$ -see front matter Ó 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.imavis.2008.04.019 mined the feature for image retrieval by analyzing the color attribute of an image [2,[5][6][7][8]10,11,13]. Color histogram [2,6,7,10] is the most commonly used feature for color-based image retrieval.…”

Section: Introductionmentioning

confidence: 99%