We study the problem of Salient Object Subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1-4). To this end, we present a salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained Convolutional Neural Network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.
Salient object detection. Salient object detection aims at detecting dominant objects in a scene. Given a test image, some methods generate a saliency map that highlights the overall region of salient objects; other methods produce bounding boxes for localization. Ideally, if a salient object detection method can well localize each salient object, then the number of objects can be simply inferred by counting the detection windows. However, many existing salient object detection methods assume the existence of salient objects, and they are mainly tested and optimized for images that contain a single dominant object . Therefore, salient object detection methods often generate undesirable results on background images, and are prone to fail on images with multiple objects and complex background. Recently, @cite_1 proposed a salient object detection method for unconstrained images. Although this method can handle complex images to some extent, we will show that the counting-by-detection approach is less effective than our subitizing method in predicting the number of salient objects.
@cite_1
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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. Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hyper graph that utilizes a set of hyper edges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyper edges in the hyper graph. The main advantage of hyper graph modeling is that it takes into account each pixel's (or region's) affinity with its neighborhood as well as its separation from image background. Furthermore, we propose an alternative approach based on center-versus-surround contextual contrast analysis, which performs salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experimental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the state-of-the-art approaches to salient object detection. In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks. Instead of generating thousands of candidate bounding boxes and refining them, our network directly learns to generate the saliency map containing the exact number of salient objects. During training, we convert the ground-truth rectangular boxes to Gaussian distributions that better capture the ROI regarding individual salient objects. During inference, the network predicts Gaussian distributions centered at salient objects with an appropriate covariance, from which bounding boxes are easily inferred. Notably, our network performs saliency map prediction without pixel-level annotations, salient object detection without object proposals, and salient object subitizing simultaneously, all in a single pass within a unified framework. Extensive experiments show that our approach outperforms existing methods on various datasets by a large margin, and achieves more than 100 fps with VGG16 network on a single GPU during inference. Image saliency detection has recently witnessed rapid progress due to deep convolutional neural networks. However, none of the existing methods is able to identify object instances in the detected salient regions. In this paper, we present a salient instance segmentation method that produces a saliency mask with distinct object instance labels for an input image. Our method consists of three steps, estimating saliency map, detecting salient object contours and identifying salient object instances. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. Once integrated with multiscale combinatorial grouping and a MAP-based subset optimization framework, our method can generate very promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database of 1000 images and their pixelwise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation. Salient object detection is a problem that has been considered in detail and many solutions proposed. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. The solution presented in this paper solves this more general problem that considers relative rank, and we propose data and metrics suitable to measuring success in a relative object saliency landscape. A novel deep learning solution is proposed based on a hierarchical representation of relative saliency and stage-wise refinement. We also show that the problem of salient object subitizing can be addressed with the same network, and our approach exceeds performance of any prior work across all metrics considered (both traditional and newly proposed). In this paper, we propose a new method for detecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works considering only local saliency detection or common pattern discovery, the proposed method integrates the local visual motion saliency extracted from each frame, global appearance consistency throughout the video, and spatiotemporal smoothness constraint on object trajectories. We first identify a temporal coherent salient region throughout the whole video, and then explicitly learn a global appearance model to distinguish the primary object against the background. In order to obtain high-quality saliency estimations from both appearance and motion cues, we propose a novel self-adaptive saliency map fusion method by learning the reliability of saliency maps from labeled data. As a whole, our method can robustly localize and track primary objects in diverse video content, and handle the challenges such as fast object and camera motion, large scale and appearance variation, background clutter, and pose deformation. Moreover, compared with some existing approaches that assume the object is present in all the frames, our approach can naturally handle the case where the object is present only in part of the frames, e.g., the object enters the scene in the middle of the video or leaves the scene before the video ends. We also propose a new video data set containing 51 videos for primary object detection with per-frame ground-truth labeling. Quantitative experiments on several challenging video data sets demonstrate the superiority of our method compared with the recent state of the arts. We aim at detecting salient objects in unconstrained images. In unconstrained images, the number of salient objects (if any) varies from image to image, and is not given. We present a salient object detection system that directly outputs a compact set of detection windows, if any, for an input image. Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects. Location proposals tend to be highly overlapping and noisy. Based on the Maximum a Posteriori principle, we propose a novel subset optimization framework to generate a compact set of detection windows out of noisy proposals. In experiments, we show that our subset optimization formulation greatly enhances the performance of our system, and our system attains 16-34 relative improvement in Average Precision compared with the state-of-the-art on three challenging salient object datasets. People can immediately and precisely identify that an image contains 1, 2, 3 or 4 items by a simple glance. The phenomenon, known as Subitizing, inspires us to pursue the task of Salient Object Subitizing (SOS), i.e. predicting the existence and the number of salient objects in a scene using holistic cues. To study this problem, we propose a new image dataset annotated using an online crowdsourcing marketplace. We show that a proposed subitizing technique using an end-to-end Convolutional Neural Network (CNN) model achieves significantly better than chance performance in matching human labels on our dataset. It attains 94 accuracy in detecting the existence of salient objects, and 42–82 accuracy (chance is 20 ) in predicting the number of salient objects (1, 2, 3, and 4+), without resorting to any object localization process. Finally, we demonstrate the usefulness of the proposed subitizing technique in two computer vision applications: salient object detection and object proposal. A key problem in salient object detection is how to effectively exploit the multi-level saliency cues in a unified and data-driven manner. In this paper, building upon the recent success of deep neural networks, we propose a fully convolutional neural network based approach empowered with multi-level fusion to salient object detection. By integrating saliency cues at different levels through fully convolutional neural networks and multi-level fusion, our approach could effectively exploit both learned semantic cues and higher-order region statistics for edge-accurate salient object detection. First, we fine-tune a fully convolutional neural network for semantic segmentation to adapt it to salient object detection to learn a suitable yet coarse perpixel saliency prediction map. This map is often smeared across salient object boundaries since the local receptive fields in the convolutional network apply naturally on both sides of such boundaries. Second, to enhance the resolution of the learned saliency prediction and to incorporate higher-order cues that are omitted by the neural network, we propose a multi-level fusion approach where super-pixel level coherency in saliency is exploited. Our extensive experimental results on various benchmark datasets demonstrate that the proposed method outperforms the state-of the-art approaches. In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. Further, we extend the proposed approach to detect a salient object from sequential images by introducing the dynamic salient features. We collected a large image database containing tens of thousands of carefully labeled images by multiple users and a video segment database, and conducted a set of experiments over them to demonstrate the effectiveness of the proposed approach. The state-of-the-art salient object detection models are able to perform well for relatively simple scenes, yet for more complex ones, they still have difficulties in highlighting salient objects completely from background, largely due to the lack of sufficiently robust features for saliency prediction. To address such an issue, this paper proposes a novel hierarchy-associated feature construction framework for salient object detection, which is based on integrating elementary features from multi-level regions in a hierarchy. Furthermore, multi-layered deep learning features are introduced and incorporated as elementary features into this framework through a compact integration scheme. This leads to a rich feature representation, which is able to represent the context of the whole object background and is much more discriminative as well as robust for salient object detection. Extensive experiments on the most widely used and challenging benchmark datasets demonstrate that the proposed approach substantially outperforms the state-of-the-art on salient object detection. 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. Recent advances of supervised salient object detection models demonstrate significant performance on benchmark datasets. Training such models, however, requires expensive pixel-wise annotations of salient objects. Moreover, many existing salient object detection models assume that at least a salient object exists in the input image. Such an impractical assumption leads to less appealing saliency maps on the background images, which contain no salient objects at all. To avoid expensive strong saliency annotations, in this paper, we study weakly supervised learning approaches for salient object detection. In specific, given a set of background images and or salient object images, where we only have annotations of salient object existence, we propose two approaches to train salient object detection models. In the first approach, we train a one-class SVM based on background superpixels. The further a superpixel is from the decision boundary of the one-class SVM, the more salient it is. The most interesting property of this approach is that we can effortlessly synthesize a set of background images to train the model. In the second approach, we present a solution toward jointly addressing salient object existence and detection tasks. We formulate salient object detection as an image labeling problem, where saliency labels of superpixels are modeled as hidden variables in the latent structural SVM framework. Experimental results on benchmark datasets validate the effectiveness of our proposed approaches. Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information. This paper addresses the problem of detecting salient areas within natural images. We shall mainly study the problem under unsupervised setting, i.e., saliency detection without learning from labeled images. A solution of multitask sparsity pursuit is proposed to integrate multiple types of features for detecting saliency collaboratively. Given an image described by multiple features, its saliency map is inferred by seeking the consistently sparse elements from the joint decompositions of multiple-feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a constrained nuclear norm and as an l2,1 -norm minimization problem, which is convex and can be solved efficiently with an augmented Lagrange multiplier method. Compared with previous methods, which usually make use of multiple features by combining the saliency maps obtained from individual features, the proposed method seamlessly integrates multiple features to produce jointly the saliency map with a single inference step and thus produces more accurate and reliable results. In addition to the unsupervised setting, the proposed method can be also generalized to incorporate the top-down priors obtained from supervised environment. Extensive experiments well validate its superiority over other state-of-the-art methods. Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps. Moreover, the proposed method is an unconstrained method able to handle images with without salient objects. Finally, we show state-of-theart performance on different salient object datasets. Typically, a salient object detection (SOD) model faces opposite requirements in processing object interiors and boundaries. The features of interiors should be invariant to strong appearance change so as to pop-out the salient object as a whole, while the features of boundaries should be selective to slight appearance change to distinguish salient objects and background. To address this selectivity-invariance dilemma, we propose a novel boundary-aware network with successive dilation for image-based SOD. In this network, the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream. Moreover, a transition compensation stream is adopted to amend the probable failures in transitional regions between interiors and boundaries. In particular, an integrated successive dilation module is proposed to enhance the feature invariance at interiors and transitional regions. Extensive experiments on six datasets show that the proposed approach outperforms 16 state-of-the-art methods. A key problem in salient object detection is how to effectively model the semantic properties of salient objects in a data-driven manner. In this paper, we propose a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (whole saliency maps). In principle, the proposed saliency model takes a data-driven strategy for encoding the underlying saliency prior information, and then sets up a multi-task learning scheme for exploring the intrinsic correlations between saliency detection and semantic image segmentation. Through collaborative feature learning from such two correlated tasks, the shared fully convolutional layers produce effective features for object perception. Moreover, it is capable of capturing the semantic information on salient objects across different levels using the fully convolutional layers, which investigate the feature-sharing properties of salient object detection with a great reduction of feature redundancy. Finally, we present a graph Laplacian regularized nonlinear regression model for saliency refinement. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches. Detection of salient object regions is useful for many vision tasks. Recently, a variety of saliency detection models have been proposed. They often behave differently over an individual image, and these saliency detection results often complement each other. To make full use of the advantages of the existing saliency detection methods, in this paper, we propose a salience learning model which combines various saliency detection methods such that the aggregation result outperforms each individual one. In our model, we first obtain several saliency maps by different saliency detection methods. The background regions of each saliency map usually lie in a low-dimensional subspace as most of them tend to have lower salience values, while the object regions that deviating from this subspace can be considered as sparse noises. So, an individual saliency map can be represented as a low rank matrix plus a sparse matrix. We aim at learning a unified sparse matrix that represents the salient regions using these obtained individual saliency maps. The sparse matrix can be inferred by conducting low rank matrix recovery using the robust principal component analysis technique. Experiments show that our model consistently outperforms each individual saliency detection approach and state-of-the-art methods.