针对稀疏重构误差算法在检测显著目标在构造背景模板时,由误选前景区域作为模板导致检测结果出现误差的问题,提出一种优化背景模板的算法.首先计算各背景模板与图像各边界的连通性,通过该边界连通性判断模板是否属于真正背景;然后用各个背景模板构成的重构字典实现对整幅图像各区域的重构,该过程采用一种新的稀疏加权算法抑制非零向量基,从而加强了解向量在相似模板中的作用;最后通过计算的各个区域重构误差产生最终的显著图.在3个标准的数据集上进行实验的结果表明,该算法有效地提升显著检测算法效果,在较为复杂的背景环境下也能产生明确的视觉显著图,与原始算法相比平均绝对误差降低近20%.%Considering extant method inaccurately selects the foreground region as the template of sparse reconstruction algorithm leading to a deviation for detecting a salient object, this paper presents a method of optimized background template. The first step is to calculate the boundaries of the connectivity of multiple regions and the image's templates are decided whether to belong to the background; then each region of the whole image is reconstructed by a dictionary with all the background templates. This process uses a novel sparse weighted method suppression of non zero vector matrix, so as to enhance the solution vector, which plays an important role between the templates. Finally, the final saliency map is generated by the calculation of the reconstruction error of each region. Experiments on the three standard datasets show that our optimi-zation algorithm can effectively improve the accuracy of the algorithm and also generate a clear saliency map even in a complicated background. Compared with the original algorithm, the mean absolute error is reduced by nearly 20%.
展开▼
