Color information for region segmentation

Ohta, Yuichi; Kanade, Takeo; Sakai, Toshiyuki

doi:10.1016/0146-664x(80)90047-7

Cited by 725 publications

(249 citation statements)

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“…[24]. In the experiments, ÿve di erent color spaces were investigated: RGB, rg chromaticity coordinates, the approximated K-L expansion-based color system (I1I2I3) introduced by Ohta et al [25], the perceptually uniform CIE 1976 (L * a * b * ), and HSV .…”

Section: Color Spacesmentioning

confidence: 99%

Classification with color and texture: jointly or separately?

Mäenpää

Pietikäinen

2004

Pattern Recognition

267

167

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Section: Color Spacesmentioning

confidence: 99%

Classification with color and texture: jointly or separately?

Mäenpää

Pietikäinen

2004

Pattern Recognition

267

167

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“…This color transformation was used in previous segmentation and texture related work ( [14], [2]) for decorrelating the color information in the RGB space. In our experiments, extracting features in the K-L space gave the best results among other spaces we have examined such as RGB, YIQ, Lab, CIE-XYZ.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…The experiment was conducted on the original, decorrelation stretched and texture sharpened versions of each sub-image. Prior to feature extraction we have converted each image from the RGB space to the K-L space [14] by applying the following transformation matrix: …”

Section: A Experimental Setupmentioning

confidence: 99%

A New Approach to Texture Recognition Using Decorrelation Stretching

Kalfon¹,

Porat²

2013

IJFCC

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Abstract-Texture features have always been a key attribute in image recognition and classification. In this paper we propose two pre-processing methods for enhancing the performance of widely used color texture recognition methods. In the first approach we propose decorrelation stretching for color enhancement, which is known to improve the interpretability of color images. The second method employs Cartoon-Texture decomposition for sharpening the texture component of the image. We show that both methods improve the classification accuracy by 7% and 4% respectively when applied to images prior to extracting auto and cross-correlation features. Our conclusion is that the proposed a p p r o a c h could be helpful in machine vision tasks.

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“…rgb-histogram. We partition the rgb plane in triangles of equal size.To cope with the instability for low intensities we follow [22] and consider points below a threshold intensity as being grey. hue-histogram.…”

Section: Photometric Stability: Robust Local Histogramsmentioning

confidence: 99%

Coloring Local Feature Extraction

Weijer

Schmid

2006

Lecture Notes in Computer Science

316

236

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Abstract. Although color is commonly experienced as an indispensable quality in describing the world around us, state-of-the art local featurebased representations are mostly based on shape description, and ignore color information. The description of color is hampered by the large amount of variations which causes the measured color values to vary significantly. In this paper we aim to extend the description of local features with color information. To accomplish a wide applicability of the color descriptor, it should be robust to : 1. photometric changes commonly encountered in the real world, 2. varying image quality, from high quality images to snap-shot photo quality and compressed internet images. Based on these requirements we derive a set of color descriptors. The set of proposed descriptors are compared by extensive testing on multiple applications areas, namely, matching, retrieval and classification, and on a wide variety of image qualities. The results show that color descriptors remain reliable under photometric and geometrical changes, and with decreasing image quality. For all experiments a combination of color and shape outperforms a pure shape-based approach.

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Color information for region segmentation

Cited by 725 publications

References 0 publications

Classification with color and texture: jointly or separately?

Classification with color and texture: jointly or separately?

A New Approach to Texture Recognition Using Decorrelation Stretching

Coloring Local Feature Extraction

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