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Previous works on palette-based color manipulation typically fail to produce visually pleasing results with vivid color and natural appearance. In this paper, we present an approach to edit colors of an image by adjusting a compact color palette. Different from existing methods that fail to preserve inherent color characteristics residing in the source image, we propose a color decomposition optimization for flexible recoloring while retaining these characteristics. For an input image, we first employ a variant of the k -means algorithm to create a palette consisting of a small set of most representative colors. Next, we propose a color decomposition optimization to decompose colors of the entire image into linear combinations of basis colors in the palette. The captured linear relationships then allow us to recolor the image by recombining the coding coefficients with a user-modified palette. Qualitative comparisons with existing methods show that our approach can more effectively recolor images. Further user study quantitatively demonstrates that our method is a good candidate for color manipulation tasks. In addition, we showcase some applications enabled by our method, including pattern colorings suggesting, color transfer, tissue staining analysis and color image segmentation.

Video synopsis aims at providing condensed representations of video data sets that can be easily captured from digital cameras nowadays, especially for daily surveillance videos. Previous work in video synopsis usually moves active objects along the time axis, which inevitably causes collisions among the moving objects if compressed much. In this paper, we propose a novel approach for compact video synopsis using a unified spatiotemporal optimization. Our approach globally shifts moving objects in both spatial and temporal domains, which shifting objects temporally to reduce the length of the video and shifting colliding objects spatially to avoid visible collision artifacts. Furthermore, using a multilevel patch relocation (MPR) method, the moving space of the original video is expanded into a compact background based on environmental content to fit with the shifted objects. The shifted objects are finally composited with the expanded moving space to obtain the high-quality video synopsis, which is more condensed while remaining free of collision artifacts. Our experimental results have shown that the compact video synopsis we produced can be browsed quickly, preserves relative spatiotemporal relationships, and avoids motion collisions.