Depth-aware salient object detection using anisotropic center-surround difference

We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-of-the-art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our SOC (Salient Objects in Clutter) dataset, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes. Finally, we report attribute-based performance assessment on our dataset.

Perceptual localization and focus refinement network for RGB-D salient object detection

Nowadays, ship detection in sea-sky background is not only useful in maritime visual surveillance, but also helpful in maritime search and rescue. Since ships are salient objects in infrared images with sea-sky background, we present a novel and effective algorithm based on saliency for ship detection in this situation. Our algorithm adopts global saliency, local saliency and background prior to generate saliency maps. Ships are finally segmented in saliency maps. Our algorithm is compared with four classic salient object detection algorithms. And experimental results show our algorithm outperforms the other four algorithms in qualitative and quantitative terms. Keyword list: ship detection ; salient object detection ; infrared image ; sea - sky background . 1. INTRODUCTION Since ships are important vehicles for sea transportation, ship detection in sea-sky background is extensively used in maritime visual surveillance and reconnaissance, avoidance of ship collision accidents, maritime search and rescue, etc. Infrared imaging systems can almost work in any environment and all-weather conditions with its visual capacity in darkness and frog, which makes it a very suitable tool in this application. In recent years, ship detection in infrared images with sea-sky background attracts increasing attention. Many methods are proposed in this field, including image segmentation methods [1-2] and morphological methods [3]. However, due to complex sea-sky environment and large variation of ship sizes, these methods cannot solve the issue completely. And since ships are often salient objects in infrared images with sea-sky background, some researchers have recently tried salient object detection ideas used in color images [4-6]. And the paper is focused on using salient object detection methods to solve the issue. Salient objects are objects which stand out in the image and attract most people ¶VLPPHGLDWHDWWHQWLRQ7KHVWDQGDUGway to detect salient ob MHFWVLVILUVWO\XVLQJVDOLHQF\GHWHFWLRQPHWKRGVWRJHQHUDWHVDOLHQF\PDSVLQZKLFKDSL[HO¶VJUH\value is proportional to its degree of saliency and then segmenting salient objects in saliency maps. Many salient detection methods can be divided into three categories: local contrast methods, global rarity methods and other methods. In local contrast methods, regions which have high contrast with local neighbouring regions are regarded as salient regions. Itti et al. [7] decompose an image into several feature maps . The saliency in each feature map is measured by local center-surround difference in several scales and saliency of different feature maps are integrated to a final saliency

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance. The authors' dataset, named SalMoN (saliency in multi-object natural images), has 588 images containing multiple objects. The subjective experiments performed record spontaneous attention and perception through eye fixation duration, point clicking and rectangle drawing. As object saliency in a multi-object image is inherently multi-level, they propose that salient object detection must be evaluated for the capability to detect all multi-level salient objects apart from the salient object class detection capability. For this purpose, they generate multi-level maps as GT corresponding to all the dataset images using the results of the subjective experiments, with the labels being multi-level salient objects and background. They then propose the use of mean absolute error, Kendall's rank correlation and average area under precision–recall curve to evaluate existing salient object detection methods on their multi-level saliency GT dataset. Approaches that represent saliency detection on images as local-global hierarchical processing of a graph perform well in their dataset.

Salient object detection (SOD) task for optical remote sensing images (RSIs) plays an important role in many remote sensing applications. Most of the existing methods train their networks depending on a large amount of pixel-wise datasets. However, such expensive and time-consuming training setting prevents the approaches becoming flexible and scalable solutions. To this end, we explore efficient SOD for optical RSIs based on easily accessible weak supervision source. In this work, we propose a novel end-to-end progressive attention-based feature recovery framework with scribble supervision. Specifically, to better locate challenging salient objects in optical RSIs, an object position module (OPM) is proposed to capture and enhance the long-range semantic dependence of objects’ position information, which depends on the complementary attention mechanism. And to restore the entire salient objects, a context refinement module (CRM) is proposed, which extract local contextual information for better propagating high-level semantics to low-level details. Moreover, to improve the adaptability of the network to the changing scenarios of optical RSIs, we propose a salient region correcting (SRC) mechanism to help the predicted salient regions rectify their saliency values by constraining the saliency relationship between predictions from different augmentation models. In addition, due to the lack of dataset for weakly supervised SOD for optical RSIs, we relabeled an existing large-scale optical RSIs dataset with scribbles, namely EORSSD-S. Experimental results on benchmark datasets demonstrate that the proposed method can outperform other weakly supervised SOD methods. And the proposed method even outperformed some fully supervised methods. https://github.com/melonless/PAFR.

Most of the existing deep learning-based methods for Mark-less human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of human body joints via 2D convolutional neural networks (CNNs). But depth map is intrinsically 3D data, treat it as 2D images will distort the shape of the actual object through projection from 3D to 2D space, and compels the network to perform perspective distortion-invariant estimation. Moreover, directly regressing 3D coordinates from a 2D image is a highly nonlinear mapping, which causes difficulty in learning procedure. To overcome these problems, a module called Supervised Endecoder is proposed to process 3D convolution data, which can also be stacked through series connection to adapt different size of dataset. Based on the module, a network called Supervised High Dimension Endecoder Network is designed, which can be used to predict key points of markless human in a single depth map in 3D space. Experiments show improved prediction accuracy compared to the state-of-the-art approaches.

Despite Convolutional Neural Networks (CNNs) based methods have been successful in detecting salient objects, their underlying mechanism that decides the salient intensity of each image part separately cannot avoid inconsistency of parts within the same salient object. This would ultimately result in an incomplete shape of the detected salient object. To solve this problem, we dig into part-object relationships and take the unprecedented attempt to employ these relationships endowed by the Capsule Network (CapsNet) for salient object detection. The entire salient object detection system is built directly on a Two-Stream Part-Object Assignment Network (TSPOANet) consisting of three algorithmic steps. In the first step, the learned deep feature maps of the input image are transformed to a group of primary capsules. In the second step, we feed the primary capsules into two identical streams, within each of which low-level capsules (parts) will be assigned to their familiar high-level capsules (object) via a locally connected routing. In the final step, the two streams are integrated in the form of a fully connected layer, where the relevant parts can be clustered together to form a complete salient object. Experimental results demonstrate the superiority of the proposed salient object detection network over the state-of-the-art methods.
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Exploiting long-range semantic contexts and geometric information is crucial to infer salient objects from RGB and depth features. However, existing methods mainly focus on excavating local features within fixed regions by continuously feeding forward networks. In this article, we introduce Dynamic Message Propagation (DMP) to dynamically learn context information within more flexible regions. We integrate DMP into a Siamese-based network to process the RGB image and depth map separately and design a multi-level feature fusion module to explore cross-level information between refined RGB and depth features. Extensive experiments show clear improvements of our method over 17 methods on six benchmark datasets for RGB-D salient object detection (SOD). Additionally, our method outperforms its competitors for the video SOD task. Code is available at https://github.com/chenbaian-cs/DMPNet .

The use of complementary information, namely depth or thermal information, has shown its benefits to salient object detection (SOD) during recent years. However, the RGB-D or RGB-T SOD problems are currently only solved independently, and most of them directly extract and fuse raw features from backbones. Such methods can be easily restricted by low-quality modality data and redundant cross-modal features. In this work, a unified end-to-end framework is designed to simultaneously analyze RGB-D and RGB-T SOD tasks. Specifically, to effectively tackle multi-modal features, we propose a novel multi-stage and multi-scale fusion network (MMNet), which consists of a cross-modal multi-stage fusion module (CMFM) and a bi-directional multi-scale decoder (BMD). Similar to the visual color stage doctrine in the human visual system (HVS), the proposed CMFM aims to explore important feature representations in feature response stage, and integrate them into cross-modal features in adversarial combination stage. Moreover, the proposed BMD learns the combination of multi-level cross-modal fused features to capture both local and global information of salient objects, and can further boost the multi-modal SOD performance. The proposed unified cross-modality feature analysis framework based on two-stage and multi-scale information fusion can be used for diverse multi-modal SOD tasks. Comprehensive experiments (<inline-formula> <tex-math notation="LaTeX">$\sim 92\text{K}$ </tex-math></inline-formula> image-pairs) demonstrate that the proposed method consistently outperforms the other 21 state-of-the-art methods on nine benchmark datasets. This validates that our proposed method can work well on diverse multi-modal SOD tasks with good generalization and robustness, and provides a good multi-modal SOD benchmark.

Salient object detection using content-sensitive hypergraph representation and partitioning

Given that existing salient object detection methods cannot effectively predict the fine contours of salient objects when extracting local or global contexts and features, we propose a novel contour self-compensated network (CSCNet) to generate a more accurate saliency map with complete contour. Unlike the common binary saliency detection, we reconstruct the salient object detection problem into a multi-classification problem of the background, the salient object, and the salient contour, where the salient contour is used as the third label for ground truth. Meanwhile, the image and its superpixel map are concatenated as the input of our network to add more edge information. Also, a penalty loss is proposed to restrict the spatial relationship between the background, objects, and their contours. Experimentally, we evaluate the proposed CSCNet on six benchmark datasets in both accuracy and efficiency and evaluate the attribute-based performance on the SOC dataset. Compared with 13 state-of-the-art algorithms, our CSCNet can detect salient objects more accurately and completely without adding too many convolutional layers and parameters.

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> salient object detection has recently received much attention for 3D scene perception owing to its omnidirectional field of view (FoV). The capability of recognizing salient objects of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> images remains technically challenging due to severe spherical distortion. In this paper, we develop a hierarchical uncertainty-aware <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> image salient object detection methodology that explicitly explores the geometric and spatial complementary coherence of Tangent projection (TP) and Equirectangular projection (ERP) by a collaborative learning strategy. Concretely, to mitigate spherical distortion, we first intend to learn saliency-related features from less-distorted tangent images, in which a deformation-aware attention block is introduced to mitigate the geometric distortion caused by projecting a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> image onto a 2D plane. However, the discrepancies among tangent images pose a new challenge to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> image salient object detection. To tackle this issue and achieve accurate localization for salient objects of all sizes, we design a spatial-frequency saliency feature aggregation module to leverage fast Fourier convolution to capture global contextual information from ERP images, such that obtaining more representative saliency features. Moreover, a hierarchical uncertainty-aware bi-projection consistency learning module with strong local-global information embedding capabilities is constructed, which learns the geometric and spatial correlations between tangent images and ERP images via a collaborative learning strategy. Ultimately, salient object maps are produced for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> images on the basis of the merged saliency features driven by the uncertainty. Extensive experiments show that our developed method improves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathrm{F}}_\beta ^{\sigma }$</tex-math></inline-formula> by an average of 31.67% compared to twenty existing advanced methods on the publicly available 360-SOD dataset.

MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images

Benefiting from the powerful features created by deep learning techniques, salient object detection has recently made significant progress. Compared with the task of detecting salient object in well-light images, the detection of salient object in low-light scenes requires not only acquiring the spatial visual saliency of images under low light conditions, but also accurately identifying the multi-scale objects of interest. Mountain Basin Network (MBNet) is proposed for salient object detection to discriminate the pixel-level saliency of low-light images. To further refine the object localization and pixel classification performance, the proposed model integrates a high-low feature aggregation module (HLFA) to synergize the information from a high level branch (named Bal-Net) and a low level branch (named Mol-Net) to fuse the global and local context, and the hierarchical supervision modules (HSM) is embedded to assist in obtaining accurate salient objects, especially the small ones. Furthermore, multi-supervised integration strategy is leveraged to optimize the structure and boundaries of salient objects. Meanwhile, to facilitate further research and evaluation of the visual saliency models, we construct a new low-light dataset, which includes 13 categories with a total of 1000 low-light images. The experimental results show that the proposed model has state-of-the-art low-light saliency detection performance compared with seven existing methods.

Salient object detection plays an important part in a vision system to detect important regions. Convolutional neural network (CNN) based methods directly train their models with large-scale datasets, but what is the crucial feature for saliency is still a problem. In this paper, we establish a novel bottom-up feature named convex hull overlap (CHO), combining with appearance contrast features, to detect salient objects. CHO feature is a kind of enhanced Gestalt cue. Psychologists believe that surroundedness reflects objects overlap relationship. An object which is on the top of the others is attractive. Our method significantly differs from other earlier works in (1) We set up a hand-crafted feature to detect salient object that our model does not need to be trained by large-scale datasets, (2) Previous works only focus on appearance features, while our CHO feature makes up the gap between the spatial object covering and the object’s saliency. Our experiments on a large number of public datasets have obtained very positive results.