Emotional image color transfer via deep learning

Liu, Da; Jiang, Yaxi; Pei, Min; Liu, Shiguang

doi:10.1016/j.patrec.2018.03.015

Cited by 43 publications

(33 citation statements)

References 15 publications

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“…In order to attack the above problems, some local color transfer methods considering the color correspondence and spatial relationship are presented in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. The local color transfer methods are divided into two groups: traditional methods and machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

“…The latter group includes color style changed with neural network [18], deep learning framework [19], and colorization with the SVM algorithm [20]. These local color transfer methods can also be classified as automatic methods [4,12,[18][19][20][21] and interactive methods [17,[22][23][24][25], where the former ones refer to the automatic local color mapping without manual intervention to region matching, and the latter means the allowance of users in defining the correspondence by sketches or rectangles. Compared with global color transfer methods, local methods usually work better in processing the textures and salient regions of an image.…”

Section: Introductionmentioning

confidence: 99%

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Color Transfer With Salient Features Mapping via Attention Maps Between Images

Ren

2020

IEEE Access

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Color transfer is a useful image editing technology that transfers the color style of a reference image to a source image. In this paper, a novel automatic perception color transfer method based on salient features mapping was proposed. Firstly, the saliency maps of the reference image and source image are built to predict the human visual attention regions. Secondly, the gradient map is calculated by convolving the image pair with Sobel filter. Thirdly, the saliency map and gradient map are combined to produce a weighted attention map. Fourthly, by thresholding the attention map the reference image and the source image are segmented into regions of salient and non-salient pixels, and mean and standard deviation of these two regions are calculated in the three channels of the YUV space, respectively. Finally, on the salient and non-salient pixels an execution similar to Reinhard's is performed, and the two results are combined to generate the final output. The experiments result show the superiority of our method by its pleasant natural appearance with color similarity and image details preservation, high time efficiency and easy implementation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Color Transfer With Salient Features Mapping via Attention Maps Between Images

Ren

2020

IEEE Access

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“…This process reproduces the model and converts the image to a matrix size of 224 × 224 × 3, which is the normal size for dataset training in CNNs. Although the X-ray image is black and white, the data are red, green, and blue using an RGB three-color system [40].…”

Section: Dataset Settingmentioning

confidence: 99%

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

2020

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Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.

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“…It affects our emotions and visual attention (Ou et al, 2004;Kopacz, 2012), so we could use it to deliver the desired information to the end user. Consequently, image colours can convey different emotions (Liu et al, 2018) and therefore influence the information value of the image. However, from the technical aspect, an image can contain myriad different colours.…”

Section: Introductionmentioning

confidence: 99%

Web-Based Application for Interactive Selection of Image Prominent Colours

Weingerl¹,

Design²,

Javoršek³

2018

Proceedings of 9th International Symposium on Graphic Engineering and Design

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An image can contain myriad different colours, but only a few of them are noticeable at first sight, hence in some way are defining the image. Therefore, the question arises which colours are prominent and what are the main factors that affect their prominence. In this paper, we present a webbased application that supports an interactive selection of colours and can be used to gather prominent colours for a set of images. The resulted database of images and their corresponding colours can be further used for investigating which image features or colour properties contribute to their prominence and for validating the models for automatic extraction of prominent colours from the images. First, the perception of prominent colours is addressed, and applied perspective of this knowledge is given. In the remainder of the paper, an overview of application architecture is presented, and a more detailed description of application's settings and usage is given. Although the main purpose of our application is to gather prominent colours of images based on the observer's opinion, the application could also be used for conducting other psychophysical experiments. The application supports three different modes for selecting the prominent colours: selection of basic hues, selection from ColorChecker Classic target and selection from custom defined patches.

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Emotional image color transfer via deep learning

Cited by 43 publications

References 15 publications

Color Transfer With Salient Features Mapping via Attention Maps Between Images

Color Transfer With Salient Features Mapping via Attention Maps Between Images

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Web-Based Application for Interactive Selection of Image Prominent Colours

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