'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank
matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First,
previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations
of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the
salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To
address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a
tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have
similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature
space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model
for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show
competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics