Color and spatial feature for content-based image retrieval

Kankanhalli, Mohan; Mehtre, Babu M.; Huang, Hock Yiung

doi:10.1016/s0167-8655(98)00100-7

Cited by 58 publications

(26 citation statements)

References 15 publications

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“…the square root of the second order instant of wavelet coefficients. The LUV color space [14,15] mentioned in this paper, to extract the color components where L encrypts luminance; U and V encrypt color information (chrominance). The other three features acquired by applying Daubechies-4 wavelet alter to the L component of the image.…”

Section: Feature Extraction and Representationmentioning

confidence: 99%

OCRM: Optimal Cost Region Matching Similarity Measure for Region Based Image Retrieval

Rao¹,

Sravani²,

Kumar³

2014

IJMUE

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Section: Feature Extraction and Representationmentioning

confidence: 99%

OCRM: Optimal Cost Region Matching Similarity Measure for Region Based Image Retrieval

Rao¹,

Sravani²,

Kumar³

2014

IJMUE

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“…Kankanhalli et al [81] presented a context based image retrieval m ethod based on colour and spatial feature distributions. A colour and spatial clustering al gorithm was adopted and the colour and spatial feature similarity of the two images were compared using a similarity measure.…”

Section: S U M M Arymentioning

confidence: 99%

A generic framework for colour texture segmentation

Nammalwar

Ghita²,

Whelan³

2010

Sensor Review

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PurposeThe purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.Design/methodology/approachThe framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.FindingsThe inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.Originality/valueThe novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

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“…Second, users are sometimes not satisfied because keyword annotation is very subjective. Against such a backdrop, various studies on content-based image retrieval have been conducted with great success [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In CBIR, image features such as color, shape and texture are used, queries such as example images and sketch images are used.…”

Section: Introductionmentioning

confidence: 99%