Image Recoloring of Art Paintings for the Color Blind Guided by Semantic Segmentation

Chatzistamatis, Stamatis; Rigos, Anastasios; Tsekouras, George E.

doi:10.1007/978-3-030-48791-1_20

Cited by 7 publications

(27 citation statements)

References 15 publications

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“…After the recoloring, the same differences as seen by a dichromat color-blind are defined between the elements of the sets

and

. To enable a dichromat to perceive the color differences in a similar way as a normal trichromat does, thus enhancing the contrast of the recolored image, the above distances must be as similar as possible [ 32 ],

…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Thus,

and

quantify the contrast enhancement of the recolored image [ 32 ]. To further improve the naturalness, the subsequent error function is employed [ 32 ],

…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Although the method enhances the color contrast, the naturalness may be compromised because the color palette utilizes only blue and yellow and their variants. In [ 32 ], a recoloring for art paintings was proposed. First, a deep learning network was used to perform transfer learning from natural images to art paintings and then a semantic segmentation approach was set up to generate annotated object recognition of art paintings.…”

Section: State Of the Art And The Current Contributionmentioning

confidence: 99%

“…As such, they accurately reflect the way a dichromat perceives colors. In contrast to other approaches that perform the recoloring only in terms of optimization [ 15 , 16 , 23 , 25 , 32 ], this paper introduces a mechanism to remove specific confusing colors to specific confusion lines, thus enhancing the contrast. Since each color is transferred to its closest non-occupied confusion line, it is expected that the naturalness will be preserved, also.…”

Section: State Of the Art And The Current Contributionmentioning

confidence: 99%

“…So far, a wide range of recoloring methods have been developed based on optimization of specially designed objective functions [ 11 , 12 , 13 , 14 , 15 , 16 ] or regularized objective functions [ 17 , 18 , 19 ] that uniformly combine the naturalness and contrast criteria, pixel-based classification [ 20 ], spectral filtering [ 21 ], cluster analysis [ 22 ], gradient domain recoloring [ 23 , 24 ], confusion-line based [ 7 , 8 , 25 ], color transformation and rotation/translation [ 26 , 27 , 28 , 29 ], neural networks [ 30 ], image retrieval [ 31 ], and deep learning [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Image Recoloring for Color Vision Deficiency

Tsekouras

Rigos

Chatzistamatis

et al. 2021

Sensors

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In this paper, a novel method to modify color images for the protanopia and deuteranopia color vision deficiencies is proposed. The method admits certain criteria, such as preserving image naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy clustering-based color segmentation extracts key colors (which are the cluster centers) of the input image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so that they can be discriminated by the color-blind. In the third module, the key colors are further adapted by optimizing a regularized objective function that combines the aforementioned criteria. Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the associated fuzzy clusters. Three related methods are compared with the proposed one, using two performance indices, and evaluated by several experiments over 195 natural images and six digitized art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative evaluation based on nonparametric statistical analysis is conducted by comparing the proposed method to each one of the other three methods for protanopia and deuteranopia, and for each index. In most of the comparisons, the Bonferroni adjusted p-values are <0.015, favoring the superiority of the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored images. (c) Subjective evaluation supports the above results.

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“…After the recoloring, the same differences as seen by a dichromat color-blind are defined between the elements of the sets

and

…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Thus,

and

quantify the contrast enhancement of the recolored image [ 32 ]. To further improve the naturalness, the subsequent error function is employed [ 32 ],

…”

Section: The Proposed Methodsmentioning

confidence: 99%

Section: State Of the Art And The Current Contributionmentioning

confidence: 99%

Section: State Of the Art And The Current Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Approach to Image Recoloring for Color Vision Deficiency

Tsekouras

Rigos

Chatzistamatis

et al. 2021

Sensors

Self Cite

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show abstract

Personalized Image Generation for Color Vision Deficiency Population

Jiang,

Liu,

et al. 2023

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

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Semantic Segmentation in Art Paintings

Cohen

Newman

Shamir

2022

Computer Graphics Forum

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Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintings, an even more challenging task because of a much larger diversity in colors, textures, and shapes and because there are no ground truth annotations available for segmentation. We propose an unsupervised method for semantic segmentation of paintings using domain adaptation. Our approach creates a training set of pseudo‐paintings in specific artistic styles by using style‐transfer on the PASCAL VOC 2012 dataset, and then applies domain confusion between PASCAL VOC 2012 and real paintings. These two steps build on a new dataset we gathered called DRAM (Diverse Realism in Art Movements) composed of figurative art paintings from four movements, which are highly diverse in pattern, color, and geometry. To segment new paintings, we present a composite multi‐domain adaptation method that trains on each sub‐domain separately and composes their solutions during inference time. Our method provides better segmentation results not only on the specific artistic movements of DRAM, but also on other, unseen ones. We compare our approach to alternative methods and show applications of semantic segmentation in art paintings. The code and models for our approach are publicly available at: https://github.com/Nadavc220/SemanticSegmentationInArtPaintings.

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Image Recoloring of Art Paintings for the Color Blind Guided by Semantic Segmentation

Cited by 7 publications

References 15 publications

A Novel Approach to Image Recoloring for Color Vision Deficiency

A Novel Approach to Image Recoloring for Color Vision Deficiency

Personalized Image Generation for Color Vision Deficiency Population

Semantic Segmentation in Art Paintings

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