Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this CVPR 2016 paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.
Salient object detection can be performed either in a bottom-up fashion using low-level features @cite_24 @cite_9 @cite_42 @cite_6 @cite_36 @cite_12 @cite_22 @cite_10 @cite_21 or in a top-down fashion via the incorporation of high-level knowledge @cite_39 @cite_31 @cite_35 @cite_40 @cite_45 @cite_25 @cite_18 . Since this paper is focused on visual saliency based on deep learning, we discuss relevant work in this context below.
@cite_35 @cite_18 @cite_22 @cite_36 @cite_9 @cite_42 @cite_21 @cite_6 @cite_39 @cite_24 @cite_40 @cite_45 @cite_31 @cite_10 @cite_25 @cite_12
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Salient object detection is not a pure low-level, bottom-up process. Higher-level knowledge is important even for task-independent image saliency. We propose a unified model to incorporate traditional low-level features with higher-level guidance to detect salient objects. In our model, an image is represented as a low-rank matrix plus sparse noises in a certain feature space, where the non-salient regions (or background) can be explained by the low-rank matrix, and the salient regions are indicated by the sparse noises. To ensure the validity of this model, a linear transform for the feature space is introduced and needs to be learned. Given an image, its low-level saliency is then extracted by identifying those sparse noises when recovering the low-rank matrix. Furthermore, higher-level knowledge is fused to compose a prior map, and is treated as a prior term in the objective function to improve the performance. Extensive experiments show that our model can comfortably achieves comparable performance to the existing methods even without the help from high-level knowledge. The integration of top-down priors further improves the performance and achieves the state-of-the-art. Moreover, the proposed model can be considered as a prototype framework not only for general salient object detection, but also for potential task-dependent saliency applications. Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper. Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-independent object candidates are segmented from the image by the quantized color attributes. Then two metrics, global cues and candidate objectness, are developed to estimate the saliency in the whole image and the object candidates respectively. We use global cues to describe the focusness and spatial distribution of color attributes in the entire image. As supplementary, candidate objectness can reveal the objectness of the object candidates. Finally, we integrate the two saliency cues to derive a saliency map of the image. By explicitly fusing candidate objectness and global cues, our proposed method is more suitable for processing images with complex background. Experimental results on three large benchmark databases demonstrate that the proposed method achieves more favorable performance than 15 recent state-of-the-art methods in terms of three evaluation criterions. We propose an object-level salient detection algorithm which explicitly explores bottom-up visual attention and objectness cues.Some category-independent object candidates are firstly segmented from the image by the quantized color attributes of images.Global cues and candidate objectness are developed, to evaluate bottom-up visual attention of the whole image and the objectness of the object candidates respectively.By explicitly combining local objectness cue with global low-level saliency cues with candidates location and color attributes, our proposed method is more suitable for processing images with complex background. Previous saliency detection algorithms used to focus on low level features directly or utilize a bunch of sample images and manually labeled ground truth to train a high level learning model. In this paper, we propose a novel coding-based saliency measure by exploring both global and local cues for saliency computation. Firstly, we construct a bottom-up saliency map by considering global contrast information via low level features. Secondly, by using a locality-constrained linear coding algorithm, a top-down saliency map is formulated based on the reconstruction error. To better exploit the local and global information, we integrate the bottom-up and top-down maps as the final saliency map. Extensive experimental results on three large benchmark datasets demonstrate that the proposed approach outperforms 22 state-of-the-art methods in terms of three popular evaluation measures, i.e., the Precision and Recall curve, Area Under ROC Curve and F-measure value. Furthermore, the proposed coding-based method can be easily applied in other methods for significant improvement. HighlightsWe present a coding-based algorithm for salient object detection.Integration of local and global cues makes the saliency maps more accurate, intact.Bottom-up maps provide foreground and background codebooks for following steps.Fusion of FC and BC based results makes the saliency results more uniform, robust.Our coding-based method can be easily applied in other methods for improvement.