自适应融合局部和全局稀疏表示的图像显著性检测

针对基于局部或全局稀疏表示的图像显著性检测方法频繁出现提取对象不完整、边界不光滑及噪声消除不干净等问题，提出自适应融合局部和全局稀疏表示的图像显著性检测方法。首先，对原始图像进行分块处理，利用图像块代替像素操作，降低算法复杂度；其次，对分块后的图像进行局部稀疏表示，即：针对每一个图像块，选取其周围的若干图像块生成过完备字典，基于该字典对图像块进行稀疏重构，得到原始图像的初始局部显著图，该显著图能够有效提取显著性目标的边缘信息；接着，对分块后的图像进行全局稀疏表示，与局部稀疏表示过程类似，不同的是针对每一个图像块所生成的字典来源于图像四周边界处的图像块，这样可以得到能有效检测出显著性目标内部区域的初始全局显著图；最后，将初始局部和全局显著图进行自适应融合，生成最终显著图。实验结果表明，提出算法在查准率（precision）、查全率（recall）及F-measure等指标上优于几种经典的图像显著性检测方法。

Image saliency detection via adaptive fusion of local and global sparse representation

To solve the problems of local or global sparse representation based image saliency detection methods, such as incomplete object extracted, unsmooth boundary and residual noise, an image saliency detection algorithm based on adaptive fusion of local sparse representation and global sparse representation was proposed. Firstly, the original image was divided into a set of image blocks, and these blocks were used to substitute the image pixels, which may decrease the computational complexity. Secondly, the blocked image was represented via local sparse representation. Specifically, for each image block, an overcomplete dictionary was generated by using its surrounding image blocks, and based on such dictionary the image block was sparsely reconstructed. As a result, an initial local saliency map which may effectively extract the edges of the salient objects could be gotten. Thirdly, the blocked image was represented by global sparse representation. The procedures were similar to the above steps. The difference was that, for each image block, the overcomplete dictionary was constructed by using the image blocks from the four margins of the input image. According to this, an initial global saliency map which could effectively detect the inner areas of the salient objects was obtained. Finally, the initial local and global saliency maps were adaptively fused together to compute the final saliency map. Experimental results demonstrate that compared with several classical saliency detection methods, the proposed algorithm significantly improves the precision, recall and F-measure.