Low-Rank Matrix Recovery (LRMR) has recently been applied to saliency detection by decomposing image features into a low-rank component associated with background and a sparse component associated with visual salient regions. Despite its great potential, existing LRMR-based saliency detection methods seldom consider the inter-relationship among elements within these two components, thus are prone to generating scattered or incomplete saliency maps. In this paper, we introduce a novel and efficient LRMR-based saliency detection model under a coarse-to-fine framework to circumvent this limitation. First, we roughly measure the saliency of image regions with a baseline LRMR model that integrates a @math -norm sparsity constraint and a Laplacian regularization smooth term. Given samples from the coarse saliency map, we then learn a projection that maps image features to refined saliency values, to significantly sharpen the object boundaries and to preserve the object entirety. We evaluate our framework against existing LRMR based methods on three benchmark datasets. Experimental results validate the superiority of our method as well as the effectiveness of our suggested coarse-to-fine framework, especially for images containing multiple objects.
Although above extensions improved algorithm robustness to cluttered backgrounds, there still remain two open problems. First, extra priors @cite_20 or complicated operations (such as saliency fusion @cite_10 @cite_45 ) may incur expensive computational cost. Second, these methods neglect the spatial relationship among image regions, which cannot ensure the entirety of detected objects. The first work that attempts to address above two limitations is the recently proposed structured matrix decomposition (SMD) by Peng @cite_46 . Specifically, SMD introduces two new regularization terms to Eq. ): a tree-structured sparse constraint that is used to preserve inter-correlations among sparse elements and a Laplacian regularization term that is adopted to enlarge the difference between foreground and background. This way, the spatial relationship among sparse elements and the coherence between low rank component and sparse component are explicitly modeled and optimized in a unified model. The objective of SMD is formulated as:
@cite_46 @cite_45 @cite_10 @cite_20
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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. To facilitate efficiency, most recent successful saliency detection methods are built on superpixel level. However, saliency detection with single-scale superpixel segmentation may fail in capturing the intrinsic salient objects in complex natural scenes with small-scale high-contrast backgrounds. To tackle this problem and realize more reliable saliency detection, we present a simple strategy using multiscale superpixels to jointly detect salient object via low-rank analysis. Specifically, we construct a multiscale superpixel pyramid and derive the corresponding saliency map using multiple saliency features and priors for each single scale at first. Then, we show that by joint low-rank analysis of multiscale saliency maps, we can obtain a more reliable adaptively fused saliency map that takes all scales saliency results into account. We further propose a GMM-based co-saliency prior to enable the above approach to detecting co-salient objects from multiple images. Extensive experiments on benchmark datasets validate the effectiveness and superiority of the proposed approach over state-of-the-art methods. In this paper, we address the problem of fusing various saliency detection methods such that the fusion result outperforms each of the individual methods. We observe that the saliency regions shown in different saliency maps are with high probability covering parts of the salient object. With image regions being represented by the saliency values of multiple saliency maps, the object regions have strong correlation and thus lie in a low-dimensional subspace. Meanwhile, most of the background regions tend to have lower saliency values in various saliency maps. They are also strongly correlated and lie in a low-dimensional subspace that is independent of the object subspace. Therefore, an image can be represented as the combination of two low rank matrices. To obtain a unified low rank matrix that represents the salient object, this paper presents a double low rank matrix recovery model for saliency fusion. The inference process is formulated as a constrained nuclear norm minimization problem, which is convex and can be solved efficiently with the alternating direction method of multipliers. Furthermore, to reduce the computational complexity of the proposed saliency fusion method, a saliency model selection strategy based on the sparse representation is proposed. Experiments on five datasets show that our method consistently outperforms each individual saliency detection approach and other state-of-the-art saliency fusion methods. Low-rank matrix recovery (LRMR) model, aiming at decomposing a matrix into a low-rank matrix and a sparse one, has shown the potential to address the problem of saliency detection, where the decomposed low-rank matrix naturally corresponds to the background, and the sparse one captures salient objects. This is under the assumption that the background is consistent and objects are obviously distinctive. Unfortunately, in real images, the background may be cluttered and may have low contrast with objects. Thus directly applying the LRMR model to the saliency detection has limited robustness. This paper proposes a novel approach that exploits bottom-up segmentation as a guidance cue of the matrix recovery. This method is fully unsupervised, yet obtains higher performance than the supervised LRMR model. A new challenging dataset PASCAL-1500 is also introduced to validate the saliency detection performance. Extensive evaluations on the widely used MSRA-1000 dataset and also on the new PASCAL-1500 dataset demonstrate that the proposed saliency model outperforms the state-of-the-art models.