Cross-Language Similarity between Perceptual and Semantic Structures of Color Tones

Giragama, C.N.W.; Marasinghe, Chandrajith Ashuboda; Madurapperuma, A.P.; Wanasinghe, Dishna R.; Herath, Susantha; Minetada, O.

doi:10.1109/icsmc.2006.384406

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…In this study conducted on Malaysian users, we used the English word descriptions from [38] which are Bright, Dull, Pale, Dark and based on our prior knowledge, and we include Pastel for another description. We have conducted an experiment on eight Malaysian Color Experts for triangulation of the results.…”

Section: Contributionmentioning

confidence: 99%

“…Focused groups research and scholars were involved in the experiments and the analysis was done on web colors. Giragama et al [38] studied on how Japanese and Sinhala native viewers describe differences in color tone (modifiers) such as bright, vivid, strong, dark, pale, and dark in their native languages. In this study, the researchers compare a color with various levels of lightness and saturation and had asked the viewers to describe the color individually.…”

Section: Introductionmentioning

confidence: 99%

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Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval

2020

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Institutions that possess certain collections of digital image libraries, such as museums, are progressively interested in making such collections accessible anytime and anywhere for any Image Retrieval (IR) activities namely browsing and searching. Many researchers have shown that IR methods, in filtering images based on their features such as colors, would provide better indexing and can be able to deliver/provide more accurate results. The color composition of an image, e.g. color histogram has proven to be a powerful feature that can be analyzed and used for image indexing because of its robust standardization of image transformation such as scaling and orientation. In this research, the efforts in narrowing the gap between low relevancy human text descriptions for Malaysian users and image scene color appearances have been brought into attention. The methods are first, to investigate the color concepts and color appearance descriptions of a scene and secondly, to identify a set of ground-truth images for each color appearance category. Psychophysical experiments are conducted to determine a collection of groundtruth images that effectively match five color appearance descriptions for image scenes in accordance with human judgement and perception. The results of the experiments are presented together with the inter-rater agreement analysis. These descriptions that are commonly queried by humans are the following keywords, Bright, Pastel, Dull, Pale, and Dark. The agreement analysis indicates that the Bright category is the most comprehensible by humans and subsequently followed by the Pastel and Dark categories. Dull and Pale categories, on the other hand are fairly understood by humans. All the images involved in this research are landscape painting collections from the internet and they are used for academic purposes only. The results show the top ten ground-truth images for each category that encapsulates a high level of agreeability between humans.

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Section: Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval

2020

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“…and the intensity component (amount of lightness and saturation) [19]. Gigarama et al [20] studied on how Japanese and Sinhala native viewers describe differences in most commonly used colour tones such as bright, vivid, strong, dark, pale and dark in their own native languages. In our previous work, we have done a small scale psychophysical experiment on human colour perception or how human perceived the colours of images.…”

Section: A Colour Appearances Categorization Of Landscapementioning

confidence: 99%

Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

Othman

Wook

Arif³

2016

International Journal on Advanced Science, Engineering and Information Technology

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In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on the Harmonic Mean of the precision and recall, also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds. The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.

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The analysis of colour appearance categories of landscape paintings for Malaysian users

Othman

Wook

Arif³

2017

2017 6th International Conference on Electrical Engineering and Informatics (ICEEI)

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Cross-Language Similarity between Perceptual and Semantic Structures of Color Tones

Cited by 4 publications

References 16 publications

Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval

Categorizing Color Appearances of Image Scenes Based on Human Color Perception for Image Retrieval

Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

The analysis of colour appearance categories of landscape paintings for Malaysian users

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