Multi-source weakly supervised salient object detection via boosting weak-annotation source and constraining object structure

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

Automatic detection of salient image regions is a useful tool with applications in intelligent camera control, virtual cinematography, video summarization and editing, evaluation of viewer preferences, and many others. This paper presents an effective method for detecting potentially salient foreground regions. Salient regions are identified by eigenvalue analysis of a graph Laplacian that is defined over the color similarity of image superpixels, under the assumption that the majority of pixels on the image boundary show non-salient background. In contrast to previous methods based on graph-cuts or graph partitioning, our method provides continuously-valued saliency estimates with complementary properties to recently proposed color contrast-based approaches. Moreover, exploiting discriminative properties of the Fiedler vector, we devise an SVM-based classifier that allows us to determine whether an image contains any salient objects at all, a problem that has been largely neglected in previous works. We also describe how the per-frame saliency detection can be extended to improve its spatiotemporal coherence when computed on video sequences. Extensive evaluation on several datasets demonstrates and validates the state-of-the-art performance of the proposed method.

This paper proposes a video retargeting method for intelligent sensing of surveillence devices. The spatiotemporal saliency model is firstly built by evaluating the contrasts between the global histograms and each regional histogram. Based on the spatiotemporal saliency map, a salient object detection method is used to locate salient object regions in the video. Then the size of cropping window is evaluated based on the moving objects. Finally cropping and scaling operations are performed on the basis of salient object regions to generate the retargeted video.

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.

Salient object detection is a process of extracting an object which is visually attractive from a single image or a video. As a powerful technique for automatic image or video segmentation, saliency detection has been focused and studied recently. In this paper, we propose a novel method for salient object detection without training or learning-based techniques. The proposed framework consists of two major steps, the generation of saliency map candidates and the selection of an optimal saliency map. To generate saliency map candidates, prior maps based on combinations of RGB color components are proposed. To select the optimal saliency map among the candidates, we propose a compactness measure, which evaluates the degree to which the generated saliency maps show objects. As a result, among recent works on saliency detection, our saliency detection method achieves the highest performance in terms of saliency detection.

Fully supervised salient object detection (SOD) methods have made considerable progress in performance, yet these models rely heavily on expensive pixel-wise labels. Recently, to achieve a trade-off between labeling burden and performance, scribble-based SOD methods have attracted increasing attention. Previous scribble-based models directly implement the SOD task only based on SOD training data with limited information, it is extremely difficult for them to understand the image and further achieve a superior SOD task. In this paper, we propose a simple yet effective framework guided by general visual representations with rich contextual semantic knowledge for scribble-based SOD. These general visual representations are generated by self-supervised learning based on large-scale unlabeled datasets. Our framework consists of a task-related encoder, a general visual module, and an information integration module to efficiently combine the general visual representations with task-related features to perform the SOD task based on understanding the contextual connections of images. Meanwhile, we propose a novel global semantic affinity loss to guide the model to perceive the global structure of the salient objects. Experimental results on five public benchmark datasets demonstrate that our method, which only utilizes scribble annotations without introducing any extra label, outperforms the state-of-the-art weakly supervised SOD methods. Specifically, it outperforms the previous best scribble-based method on all datasets with an average gain of 5.5% for max f-measure, 5.8% for mean f-measure, 24% for MAE, and 3.1% for E-measure. Moreover, our method achieves comparable or even superior performance to the state-of-the-art fully supervised models.

The performance of recent RGB-D salient object detectors has significantly improved owing to their integration of convolutional neural networks (CNNs). However, most existing salient object detection (SOD) methods represent features using a VGGNet backbone, which lacks the ability to retain complete RGB and depth modals and must compensate by applying several skip connections. In this letter, we propose a gate fusion network (GFNet) with Res2Net architecture to solve this problem. GFNet consists of two interacting Res2Net block encoder streams and four gate fusion block (GFB) decoders to interconnect the streams and fuse features. Res2Net blocks have a robust feature retention mechanism to ensure that the decoders can learn complete information, while the GFB formulates the interdependences of the encoders and eliminates noise via a gate mechanism. We evaluated GFNet using two popular RGB-D salient detection benchmark datasets (NJU2000 and NLPR) and achieved state-of-the art performance.

We present a novel, robust estimation method to distinguish salient objects from complicated, dynamic backgrounds in videos. In this method, we propose a novel approach to model motion energy based on motion magnitude, motion orientation, gradient flow field, and spatial gradient of the video frame. Furthermore, an effective spatiotemporal objectness map is also proposed to estimate a compact object-like region in the current video frame leveraging both the objectness proposals and the saliency map of the previous frame. Then the current video frame is oversegmented into the granularity of superpixels using the simple linear iterative clustering algorithm. Each superpixel is designated as a node of a graph. The similarity between adjacent superpixels will be assigned as the weight of an edge that connects these two nodes. The feature values of motion energy and spatiotemporal objectness within each superpixel will be averaged respectively, and used to graphically cluster similar superpixels to form the detected salient object. Extensive experiments comparing this proposed new method against twelve existing salient object detection (SOD) methods have been performed using the benchmark datasets unconstrained video saliency detection and densely annotated video segmentation. Superior performance of this proposed SOD method has been observed through three well-known performance metrics: precision-recall curves, F-measure curves, and the mean absolute error.

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Underwater images contain abundant information, but many challenges remain for underwater object detection tasks. Various salient object detection methods may encounter low detection precision, and the segmented map has an incomplete region of the target object. To deal with blurry underwater scenes and vague detection problems, a novel fusion underwater salient object detection algorithm (FUSOD) is proposed based on multi-scale saliency and spatial optimization. Firstly, an improved underwater color restoration was utilized to restore the color information for afterward color contrast saliency calculation. Secondly, a more accurate multi-scale fusion saliency map was obtained by fully considering both the global and local feature contrast information. Finally, the fusion saliency was optimized by the proposed spatial optimization method to enhance the spatial coherence. The proposed FUSOD algorithm may process turbid and complex underwater scenes and preserve a complete structure of the target object. Experimental results on the USOD dataset show that the proposed FUSOD algorithm can segment the salient object with a comparatively higher detection precision than the other traditional state-of-the-art algorithms. An ablation experiment showed that the proposed spatial optimization method increases the detection precision by 0.0325 scores in the F-Measure.

We propose visual comfort assessment for stereoscopic 3D (S3D) images based on salient discomfort regions. Color-based saliency successfully represents visual attention because that the human visual system (HVS) focuses on the most salient region in an image. Disparity-based saliency effectively expresses visual comfort in S3D images. Based on two saliencies, we extract salient discomfort regions which mostly determine the overall visual comfort degree in S3D images. Then, we predict the visual comfort score from salient discomfort regions by a disparity feature vector which combines saliency-weighted disparity and maximum disparity. Finally, we provide visual comfort index maps of S3D images based on visual comfort scores, thus showing perceptually salient discomfort regions and their degrees. Experimental results demonstrate that the proposed method achieves significantly high accuracy in visual comfort assessment as compared to existing methods.

Since most existing saliency detection models are not suitable for the condition that the salient objects are near at the image border, the authors propose a saliency detection approach based on adaptive background template (SCB) despite of the position of the salient objects. First, a selection strategy is presented to establish the adaptive background template by removing the potential saliency superpixels from the image border regions, and the initial saliency map is obtained. Second, a propagation mechanism based on K‐means algorithm is designed for maintaining the neighbourhood coherence of the above saliency map. Finally, a new spatial prior is presented to integrate the saliency detection results by aggregating two complementary measures such as image centre preference and the background template exclusion. Comprehensive evaluations on six benchmark datasets indicate that the authors’ method outperforms other state‐of‐the‐art approaches. In addition, a new dataset containing 300 challenging images is constructed for evaluating the performance of various salient object detection methods.

Due to the rapid development of deep learning, salient object detection has achieved promising results in recent years. However, how to construct a powerful saliency detection network to generate saliency maps with completely highlighted salient objects and effectively suppressed background noise still remains an open and challenging problem. In this paper, we propose a novel trifurcated cascaded refinement network (TCRNet) to explore multi-level feature fusion and global information representation. Specifically, all the side-output features from the backbone network are divided into three branches (i.e., global information, deep information and shallow information) according to feature similarities, and then the global contexts, semantic knowledge and fined details respectively generated by the three branches are integrated in a cascaded refinement way. This refinement strategy aims to guide the feature learning of important regions and suppress background noises. Moreover, we propose a cross-level feature fusion (CFF) module to fuse multi-level similar features within a same branch by exploring their interrelationship, thus capturing the important semantic information and structure details. To this end, the complementarity of similar features is utilized to refine feature at each level by sharing an encoded spatial weight map. Besides, we design a context-aware feature learning (CFL) module to capture global contexts in a single layer by taking account of both single-feature representation and multi-feature interaction, thus providing important location information of salient objects. Extensive experiments on five benchmarks are conducted to demonstrate that the proposed network TCRNet performs favorably against 20 state-of-the-art salient object detection methods on five benchmark datasets, indicating the effectiveness and superiority of our method.

Automatic extraction of salient regions is beneficial for various computer vision applications, such as image segmentation and object recognition. The salient visual information across images is very useful and plays a significant role for the visually impaired in identifying tactile information. In this paper, we introduce a novel saliency cuts method using local adaptive thresholding to obtain four regions from a given saliency map. First, we produced four regions for image segmentation using a saliency map as an input image and local adaptive thresholding. Second, the four regions were used to initialize an iterative version of the GrabCuts algorithm and to produce a robust and high-quality binary mask with a full resolution. Finally, salient objects’ outer boundaries and inner edges were detected using the solution from our previous research. Experimental results showed that local adaptive thresholding using integral images can produce a more robust binary mask compared to the results from previous works that make use of global thresholding techniques for salient object segmentation. The proposed method can extract salient objects with a low-quality saliency map, achieving a promising performance compared to existing methods. The proposed method has advantages in extracting salient objects and generating simple, important edges from natural scene images efficiently for delivering visually salient information to the visually impaired.

Image segmentation is a well-developing topic in the image processing, and a number of previous works have been proposed and achieved high performance. However, most previous works needed user-assistance to provide the prior information of the target object in the segmentation. In this paper we propose an unsupervised scheme, combining the salient object detection and segmentation method, to segment the target object without any prior information from users. The experimental results show that the proposed salient color model derived with salient features can provide a prior information with high confidence to generate precise segmentation automatically. The proposed color model of salient objects can not only be applied with Min-Cut algorithm, but also extended to more segmentation algorithms, like matting or non-parametric model.