Color Based Image Retrieval System

Kaur, Pawandeep; Thakral, Sakshi; Singh, Mandeep

doi:10.9790/0661-0150105

Cited by 3 publications

(2 citation statements)

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“…Modeling that refers to hardware includes RGB (Red Green Blue), CMY (Cyan Magenta Yellow) and YIQ. Whereas those referring to users include HLS (Hue Saturation Luminance), HCV, HSV (Hue Saturation value), HSB, MTM, CIE-LAB, and CIE-LUV [15]. In the storage process, most image file formats (JPEG, BMP, GIF) use the RGB color space.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Based on Color Dissimilarity

Karma¹,

Putra

Sudarma

et al. 2022

IJCAI

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This study aims to develop a segmentation technique that can be used to detect objects in an image. The concept used is to imitate the human concept of recognizing an object based on its color difference. A color is considered different if it has different R, G or B values. Humans can only distinguish two colors clearly if they both have a DeltaE value of at least 8. The difference in DeltaE values is obtained from differences in the values of R, G or B, either alone or in pairs. The application of this concept to the segmentation technique has shown good results.This technique is able to give the same results and is good for images with JPG, PNG and BMP file types. To provide confidence in the performance of this method, a comparison is made with several segmentation methods. By measuring the similarity index using the Jaccard index, Sørensen-Dice index and BF Score, this color dissimilarity-based segmentation method has the best performance. Meanwhile, when measuring the processing time, this method has the second best performance. Thus, it can be concluded that this method is very suitable for detecting objects in an image. Povzetek: Tehnika segmentacije je bila razvita z imitacijo človeškega koncepta prepoznavanja predmeta, in sicer na podlagi barvnih razlik. Barve se štejejo za različne, če imajo vrednost DeltaE najmanj 8, ki jo dobimo iz razlik v vrednostih R, G ali B barv samih ali v parih. Izvajanje te tehnike kaže enake rezultate za datoteke JPG, PNG in BMP in je v veliko pomoč pri postopku identifikacije predmeta.

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Section: Introductionmentioning

confidence: 99%

Image Segmentation Based on Color Dissimilarity

Karma¹,

Putra

Sudarma

et al. 2022

IJCAI

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“…Color is identified in three-dimensional color space, which is then modeled into 2 types namely hardware-oriented and user-oriented color space. For types of hardware-oriented color space include RGB (Red Green Blue), CMY (Cyan Magenta Yellow) and YIQ, while user-oriented color space types include HLS (Hue Saturation Luminance), HCV, HSV (Hue Saturation value), HSB, MTM, CIE-LAB, and CIE-LUV [13]. Most image file formats (JPEG, BMP, GIF) use the RGB color space.…”

Section: Introductionmentioning

confidence: 99%

Determination and Measurement of Color Dissimilarity

Karma¹

2020

IJEET

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There are millions of different colors that exist in this nature. There are colors that can easily be distinguished from other colors, but many are also difficult to distinguish. The ability to distinguish colors is important in the recognition of an object, especially objects in an image. Color can be represented in a three-dimensional RGB color space and each color will have an RGB value with a range of values ??from 0-255. Conceptually, colors with different RGB values ??are different colors, but the human eye may not necessarily be able to distinguish them. This study aims to determine and measure the color dissimilarity that can be distinguished by humans. With an experiment comparing a color with a color made with a variation of RGB values, this study resulted that two colors that have a Delta E (?E) value of 8 can be distinguished by the eye. This result is obtained if both colors have a value of ?R, ?G or ?B of at least 8, or a value of ?RG, ?RB or ?GB of at least 6, or a value of ?RGB of at least 5.

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