Artistic Recoloring of Image Oversegmentations

Artistic Recoloring of Image Oversegmentations Rosa Azami* David Mould† Carleton University Carleton University Figure 1: Recoloring of region-based abstraction. The original image is on the left; on the right, we show results from our method using three different palettes. ABSTRACT We propose a method to assign vivid colors to regions of an oversegmented image. We restrict the output colors to those found in an input palette, and seek to preserve the recognizability of structure in the image. Our strategy is to match the color distances between the colors of adjacent regions with the color differences between the assigned palette colors; thus, assigned colors may be very far from the original colors, but both large local differences (edges) and small ones (uniform areas) are maintained. We use the widest path algorithm on a graph-based structure to obtain a Figure 2: Colorful representational images. A modern vector illustration of a spanning tree over the set of regions, then traverse the tree to assign lion; a Fauvist painting by Andre´ Derain; an abstraction of the Eiffel Tower colors in a greedy fashion. Our method produces vivid recolorings by Robert Delaunay. of region-based abstraction using arbitrary palettes. We demonstrate a set of stylizations that can be generated by our algorithm. Manual recoloring of oversegmented images would be tedious. Keywords: Non-photorealistic rendering, Image stylization, Recol- The images contain hundreds or thousands of segments; clicking oring, Abstraction. on every segment would take a long time, even leaving aside the cognitive and interaction overhead of making selections from the Index Terms: I.3.3 [Picture/Image Generation]—; I.4.6 [Segmen- palette. We provide an automatic assignment of colors to regions. tation] The assignment can be used as is, could be used in a fast manual assessment loop (for example, if an artist wanted to choose a suitable 1 INTRODUCTION palette for coloring a scene), or could be a good starting point for a Color plays an important role in image aesthetics. In representational semi-automatic approach where a user made minor modifications to art, artists employ colors that match the perceived colors of objects in the automated results. the depicted scene. Conversely, abstraction provides the freedom to This paper presents an automatic recoloring approach for a region- use arbitrary colors. Figure 2 shows a modern vector illustration of a based abstraction. The input is a desired palette and an overseg- lion, an example of Fauvism by Andre´ Derain, and an abstraction of mented image. The method assigns a color from the palette to each the Eiffel Tower by Robert Delaunay. The artists have expressed the region; it is based on the widest path algorithm [18], which organizes image content with vivid colors disconnected from the object colors. the regions into a tree based on the weight of the edges connecting We aim to generate colorful images, recoloring an image using an them. We use color differences between adjacent regions both to arbitrary input palette. Our proposed method uses a subdivision of order the regions and to select colors, trying to match the difference the image into distinct segments, assigning each segment a palette magnitude between the assigned palette colors and the original re- color in such a way as to preserve visibility of key structures. gion colors. The use of color differences allows structures in the Our goal is to present different recoloring possibilities by assign- image to remain recognizable despite breaking the link between the ing colors to each region of an oversegmented input image. We aim original and depicted color. to maintain the image contrast and preserve strong edges so that the Our main contributions are the following: content of the scene remains recognizable. It is important to convey • We designed a recoloring method for an oversegmented image, textures and small features, often too delicate to be preserved by creating multiple abstractions colored with just one palette. existing abstraction methods. We would like to be able to create Our method creates wild and high contrast images. wild and vivid abstractions through use of unusual palettes. • Various styles can be created by our method. We experiment *e-mail: [email protected] with color blending between regions and produce smooth im- †e-mail: [email protected] ages. In addition, we generate new colorings from a palette by applying different metrics and color spaces. The remainder of the paper is organized as follows. In Section 2, we briefly present related work. We describe our algorithm in Sec- tion 3. Section 4 shows results and provides some evaluation, and Playful Palette [24] is a system for users to create rich palletes, Section 5 gives some possible variations of the method. Finally, we representing a palette as a set of blobs of color that blend together conclude in Section 6 and suggest directions for future work. to create gradients. Users manipulate the blobs to obtain the desired range of colors. DiVerdi et al. [6] proposed an approximation 2 PREVIOUS WORK of image colors based on the Playful Palette. In this technique, Although there is an existing body of work on recoloring pho- within an optimization framework, an objective function minimizes tographs [5, 8, 10, 12, 16, 20, 23, 27, 30] and researchers have in- the distance between the original image and that recolored one by vestigated recoloring in the context of non-photorealistic render- palette colors, based on the self organizing map. The approximation ing [2, 7, 19, 24, 26, 31, 32], there is room for further exploration. algorithm is an order of magnitude faster than Playful Palette. Existing recoloring methods little address region-based abstractions. Specifically assigning colors to regions has been investigated by The closest approach to ours, by Xu and Kaplan [29], used opti- previous researchers. Qu et al. [19] proposed a colorization tech- mization to assign black or white to each region of an input image. nique for black and white manga using the Gabor wavelet filter; a Below, we review some of the previous research on recoloring meth- user scribbles on the drawing to connect the regions, and the algo- ods in NPR and color palette selection. These approaches can be rithm then assigns colors to different hatching patterns, halftoning, broadly classified into example-based recoloring and palette-based and screening. Xu and Kaplan introduced artistic thresholding [29] recoloring. where an image is segmented, and each segment assigned either black or white color through an optimization process. Lin et al. [11] Example based Recoloring (Color Transfer) proposed a palette-based recoloring method with a probabilistic model. They learn and predict the distribution of properties such as Recoloring methods were first proposed by Reinhard et al. [20], saturation, lightness, and contrast for individual regions and neigh- where the colors of one image were transferred to another. They boring regions, then score pattern colorings using the predicted converted input RGB signals to Ruderman et al.’s [21] perception- distributions and color compatibility model of O’Donovan et al. [17]. based color space Lab, then shifted and scaled the Lab space using Bohra and Gandhi [3] proposed an exemplar-based colorization simple statistics. algorithm for grayscale graphic art from a reference image based Neumann and Neumann [16] extracted palette colors from an on color graph and composition matching. They retrieve palettes arbitrary target image and applied 3D histogram matching. They using the spatial features of the input image. They aim to preserve attempt to keep the original hues after style transformation: all the artist’s intent in the composition of different colors and spatial colors with the same starting hue should have the same hue after adjacency between colors in the image. transformation. However, the histogram lacks spatial information and hence is not sufficient for accurate style cloning. They suggest using image segmentation and smoothing the color histogram to 3 RECOLORING ALGORITHM improve the result. Our recoloring algorithm automatically assigns colors to regions of Levin et al. [10] introduced an interactive colorization method an oversegmented image. The system takes as input a set of regions for greyscale images. They used a quadratic cost function derived and a color palette and assigns a color from the palette to each from the color differences between a pixel and its neighborhood. region. The recolored image should convey recognizable objects User scribbles indicate the desired color in the interior of the re- in the image. Edges are essential to the visibility of the structures. gion and the colors propagate to the remaining pixels. Inspired by Neighboring regions will be assigned distinct colors to express an Levin et al. [10], Yatziv and Sapiro [30] presented a method based edge, and regions of similar colors will be assigned similar colors. on luminance-weighted chrominance blending and fast geodesic The human visual system is sensitive to brightness contrast; to help distance computations. Sykora et al. [26] developed LazyBrush for preserve contrast in our recoloring, we take into account the regions’ coloring cartoons, integrating textures into images to create 3D-like relative luminances when selecting region colors. effects [25]. Casaca et al. [4] used Laplacian coordinates for im- We concentrate on color differences between adjacent regions, age division and used a color theme for fast colorization. Fang et seeking to match the original color distances without preserving the al. [7] proposed an interactive optimization method for colorizing colors themselves. Neighbouring regions with large differences will hand-drawn grayscale images. be assigned distant colors, preserving the boundary, while similar- Color compositing has been investigated by NPR researchers. colored regions will be assigned similar output colors or even the Compositing can be done using alpha blending [14] or the Kubelka- same color.

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