Affective Color Transfer Based on Skin Color Preservation

[[abstract]]As we know, color, one of important features for composing images, can affect people on emotional level. Photographers and designers can enhance desired color in their work to convey feeling, especially in wedding pictures. In this paper, affective color transfer is proposed; we focus on implementing color transfer in wedding pictures to make overall color theme of input image be similar with reference image. The proposed skin color preserving phase prevents skin color from over-modification in original color transfer. Furthermore, we attach importance about the harmony of output image, combing both the opacity of input and output image in color transfer to reduce visual distortion. Another mechanism is affective analysis in images. First, we define affective classes and then extract affective colors in the image to classify the affective class. By using saliency map, we extracted the affective color in the image exactly. Finally, experiment results of affective analysis and affective color transfer have confirmed the effectiveness of our proposed method.[[conferencetype]]國際[[conferencedate]]20131102~20131104[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Aizu-Wakamatsu, Japa

Farbe : eine Arbeitsbibliografie

Farbe: Eine Arbeitsbibliografie. Zusammengestellt von Hans J. Wulff. Für Hinweise danke ich Christine Noll Brinckmann und Jason Grant McKahan

Who Owns the Images? The Paradox of Archives, between Commercialization, Free Circulation and Respect

Digitization carries the utopian promise of archival access unlimited by constraints of space and time, and with it, of new forms of research and historiographies. In reality, digital image archives pose a complex set of technical, legal, ethical and methodological challenges, particularly for film and media studies and adjacent fields. In a series of studies and interviews with practitioners, scholars and theorists, this volume draws a detailed map of these challenges and offers perspectives for further research and creative practice

A review of image and video colorization: From analogies to deep learning

Image colorization is a classic and important topic in computer graphics, where the aim is to add color to a monochromatic input image to produce a colorful result. In this survey, we present the history of colorization research in chronological order and summarize popular algorithms in this field. Early works on colorization mostly focused on developing techniques to improve the colorization quality. In the last few years, researchers have considered more possibilities such as combining colorization with NLP (natural language processing) and focused more on industrial applications. To better control the color, various types of color control are designed, such as providing reference images or color-scribbles. We have created a taxonomy of the colorization methods according to the input type, divided into grayscale, sketch-based and hybrid. The pros and cons are discussed for each algorithm, and they are compared according to their main characteristics. Finally, we discuss how deep learning, and in particular Generative Adversarial Networks (GANs), has changed this field

Example-based image colorization via automatic feature selection and fusion

Image colorization is an important and difficult problem in image processing with various applications including image stylization and heritage restoration. Most existing image colorization methods utilize feature matching between the reference color image and the target grayscale image. The effectiveness of features is often significantly affected by the characteristics of the local image region. Traditional methods usually combine multiple features to improve the matching performance. However, the same set of features is still applied to the whole images. In this paper, based on the observation that local regions have different characteristics and hence different features may work more effectively, we propose a novel image colorization method using automatic feature selection with the results fused via a Markov Random Field (MRF) model for improved consistency. More specifically, the proposed algorithm automatically classifies image regions as either uniform or non-uniform, and selects a suitable feature vector for each local patch of the target image to determine the colorization results. For this purpose, a descriptor based on luminance deviation is used to estimate the probability of each patch being uniform or non-uniform, and the same descriptor is also used for calculating the label cost of the MRF model to determine which feature vector should be selected for each patch. In addition, the similarity between the luminance of the neighborhood is used as the smoothness cost for the MRF model which enhances the local consistency of the colorization results. Experimental results on a variety of images show that our method outperforms several state-of-the-art algorithms, both visually and quantitatively using standard measures and a user study