Background-based Saliency Map Research Articles

Salient object detection via cross diffusion-based compactness on multiple graphs

The diffusion-based graph has been widely used in saliency detection. Most of the existing methods treat the image boundary patches as background seeds, which may result in the imprecise saliency map if the salient region touches the image boundaries. In this paper, we propose a salient object detection approach via cross diffusion-based compactness on multiple graphs. Firstly, we extract multi-view image features including low-level image features, mid-level image cues (low-level saliency priors), and background-based saliency map. Then, we compute the respective similarity matrix to construct the corresponding graph, and a cross-diffusion algorithm is presented that diffuses each similarity matrix on other’s graphs rather than at itself graph, helping to the compactness-based saliency maps. Additionally, for well propagating the saliency values, we model a propagation mechanism based on cellular automata, by linearly incorporating low-level image features and mid-level image cues together to generate a superior impact factor matrix. Extensive experiment results demonstrate that the proposed method achieves better saliency detection performance against the unsupervised state-of-the-art methods on three public datasets.

Salient object detection via two-stage absorbing Markov chain based on background and foreground

This paper proposes a saliency detection method via two-stage absorbing Markov chain based on background and foreground for detecting salient objects in images. Firstly, image preprocessing is performed, followed by convex hull construction and superpixel segmentation, to prepare for subsequent processing. Secondly, according to the boundary connectivity, the superpixels with lower background probability value in the candidate boundary background set B0 are deleted, and the boundary background set B1 is obtained. With the saliency values of the nodes in the boundary-prior saliency map Sbg1, the background seeds are added appropriately in the region outside the candidate boundary background set B0 and the convex hull H, and the background seed set B is obtained after update. Then, the background-absorbing Markov chain is constructed to generate background-absorbing saliency map Sbg2. By fusing the saliency maps Sbg1 and Sbg2, the first-stage background-based saliency map Sbg is obtained. Thirdly, in the range of the convex hull H, the foreground seed set F is determined according to the saliency map Sbg. Then, the foreground-absorbing Markov chain is constructed, to obtain the second-stage foreground-absorbing saliency map Sfg. Finally, the saliency maps Sbg and Sfg of the two stages are combined to obtain a fused saliency map S, and the final saliency map S∗ is obtained after optimization through smoothing mechanism. Compared with the traditional methods, the performance of the proposed method is significantly improved. The proposed method is tested on three public image datasets, and it shows great accuracy in detecting salient objects.

Integrating manifold ranking with boundary expansion and corners clustering for saliency detection of home scene

In this paper, we propose a novel framework for saliency detection of home scene by exploiting manifold ranking, boundary expansion, and corners clustering. Our proposed method firstly combines color cues in RGB and CIELab to select image boundary seeds, and exclude the ones which might be located at salient objects as much as possible. Then, we utilize the boundary seeds on each image boundary as the queries of manifold ranking to compute saliency and integrate them for a background-based saliency map. For the foreground-based saliency detection. Boundary expansion combined with background-based saliency map highlights foreground regions, which are regarded as queries for a foreground-based saliency map. Moreover, we achieve center prior saliency map through multi-scale Harris corner detection and corners clustering to further highlight salient regions and suppress background regions. Finally, we integrate the three saliency maps via the proposed unified framework for a more accurate and smooth saliency map. Both qualitative and quantitative experimental results indicate that our proposed method can deliver better performance than several state-of-the-art saliency detection methods as a whole.

Saliency detection via background prior and foreground seeds

As an image pre-processing technology, saliency detection (DS) can be used in a wide variety of visual tasks. A bottom-up method of DS via background prior and foreground seeds is proposed. To highlight the object and suppress the background noise, two saliency maps are obtained by using the background prior and foreground seeds. First, we use global colour and spatial distance matrices to compute a background saliency map. To evenly emphasize the saliency region, the single-layer cellular automata is used to refine the background-based saliency map. Second, a set of foreground seeds is obtained from the refined background-based saliency map. Then, the foreground-based saliency map is calculated based on the foreground seeds and refined by biased Gaussian filtering. The proposed method could emphasize the foreground target, as well as restrain the background noise. The experiment results show that our method has good ability in saliency detection.

Combining multi-layer integration algorithm with background prior and label propagation for saliency detection

In this paper, we propose a novel approach to automatically detect salient regions in an image. Firstly, some corner superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the background labels based on ranking algorithm. Subsequently, we further employ an objectness measure to pick out and propagate foreground labels. Furthermore, an integration algorithm is devised to fuse both background-based saliency map and foreground-based saliency map, meanwhile an original energy function is acted as refinement before integration. Finally, results from multiscale saliency maps are integrated to further improve the detection performance. Our experimental results on five benchmark datasets demonstrate the effectiveness of the proposed method. Our method produces more accurate saliency maps with better precision-recall curve, higher F-measure and lower mean absolute error than other 13 state-of-the-arts approaches on ASD, SED, ECSSD, iCoSeg and PASCAL-S datasets.

Saliency detection integrating both background and foreground information

In this paper, we propose a novel saliency detection algorithm. The saliency of an image element is defined not only as its contrast to the background but as its similarity to the foreground. First, we extract background seeds as well as their spatial layout information from image boundaries to compute the background-based saliency map. Second, we generate a compact foreground region from the first-stage saliency map to describe the appearance and location of the salient object and calculate the foreground-based saliency map accordingly. We integrate these two saliency maps and further refine the unified one to obtain a more smooth and accurate saliency map. Each component of the presented algorithm is evaluated on the public available datasets and the experimental results also show that the presented algorithm achieves favorable performance compared to the state-of-the-art methods.

Saliency detection via background and foreground seed selection

In this paper, we propose a bottom-up visual saliency detection algorithm. Different from most previous methods that mainly concentrate on image object, we take both background and foreground into consideration. First, we collect background seeds from image border superpixels by boundary information and calculate a background-based saliency map. Second, we select foreground seeds by segmenting the first-stage saliency map via adaptive threshold and compute a foreground-based saliency map. Third, the two saliency maps are integrated by the proposed unified function. Finally, we refine the integrated result to obtain a more smooth and accurate saliency map. Moreover, the unified formula also proves to be effective in combining the proposed approach with other models. Experiments on publicly available data sets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.