Example-based face sketch synthesis technology generally requires face photo-sketch images with face alignment and size normalize. To break through the limitation, we propose a global face sketch synthesis method: In training, all training photo-sketch patch pairs are collected together and a photo feature dictionary is learned from the photo patches. For each atom of the dictionary, its K closest photo-sketch patch pairs are clustered, namely “Anchored Neighborhood”. In testing, for each test photo patch, we search its nearest photo patch in the Anchored Neighborhood determined by its closest atom, then the corresponding sketch patch is the output. By the same way, we train and test in the high-frequency domain and synthesis the high-frequency results. Finally, the fusion of the initial and the high-frequency results is the final sketch. The experiments on three public face sketch datasets and various real-world photos demonstrate the effectiveness and robustness of the proposed method
In this paper, a novel methodology is presented to settle the region of interest (ROI) detection problem in vehicle color recognition so as to remove the redundant components of vehicles that interfere greatly with color recognition. In order to make full use of the local color and spatial information, vehicle images are divided into different superpixels at first. The spatial relationship between superpixels and the outermost pixels is then used for the background removal of vehicle images. By comparing with the vehicle window clustering centroids obtained by k-means, the superpixels close to the universal color characteristics of windows are removed so that the dominant color superpixels are determined. Finally, a linear Support Vector Machine classifier is trained for color recognition. The experiments demonstrate that the proposed methodology is effective for color region of interest detection and thus contribute to vehicle color recognition.