A novel approach to combine features for salient object detection using constrained particle swarm optimization

Despite significant amount of research works, the best available visual attention models still lag far behind human performance in predicting salient object. In this paper, we present a novel approach to detect a salient object which involves two phases. In the first phase, three features such as multi-scale contrast, center-surround histogram and color spatial distribution are obtained as described in Liu et al. model. Constrained Particle Swarm Optimization is used in the second phase to determine an optimal weight vector to combine these features to obtain saliency map to distinguish a salient object from the image background. To achieve this, we defined a simple fitness function which highlights a salient object region with well-defined boundary and effectively suppresses the background regions in an image. The performance is evaluated both qualitatively and quantitatively on a publicly available dataset. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of precision, recall, F -measure and area under curve.     

基于邻域优化机制的图像显著性目标检测

在显著性目标检测中,背景区域和前景区域区分度不高会导致检测结果不理想。针对这一问题,提出一种基于邻域优化机制的图像显著性目标检测算法。首先对图像进行超像素分割;然后在CIELab颜色空间建立对比图和分布图,并通过一种新的合并方式进行融合;最后在空间距离等约束下,建立邻域更新机制,对初始显著性图进行优化。实验对比表明,该算法显著性目标检测效果更好。     

Salient object detection method using random graph

In this paper, a bottom-up salient object detection method is proposed by modeling image as a random graph. The proposed method starts with portioning input image into superpixels and extracting color and spatial features for each superpixel. Then, a complete graph is constructed by employing superpixels as nodes. A high edge weight is assigned into a pair of superpixels if they have high similarity. Next, a random walk prior on nodes is assumed to generate the probability distribution on edges. On the other hand, a complete directed graph is created that each edge weight represents the probability for transmitting random walker from current node to next node. By considering a threshold and eliminating edges with higher probability than the threshold, a random graph is created to model input image. The inbound degree vector of a random graph is computed to determine the most salient nodes (regions). Finally, a propagation technique is used to form saliency map. Experimental results on two challenging datasets: MSRA10K and SED2 demonstrate the efficiency of the proposed unsupervised RG method in comparison with the state-of-the-art unsupervised methods.     

Salient object detection using the phase information and object model

Multimedia Tools and Applications - One of the most important features of saliency detection algorithms is to reduce the size of processing data for algorithms with higher processing size such as...     

Correction to: Salient object detection using the phase information and object model

Multimedia Tools and Applications - The article Salient object detection using the phase information and object model, written by Hooman Afsharirad and Seyed Alireza Seyedin, was originally...     

Salient object detection using feature clustering and compactness prior

Multimedia Tools and Applications - Salient object detection has been challenging computer vision though some advances have been made recently. In this study, we propose a novel salient object...     

Salient object detection using content-sensitive hypergraph representation and partitioning

As an important problem in image understanding, salient object detection is essential for image classification, object recognition, as well as image retrieval. In this paper, we propose a new approach to detect salient objects from an image by using content-sensitive hypergraph representation and partitioning. Firstly, a polygonal potential Region-Of-Interest (p-ROI) is extracted through analyzing the edge distribution in an image. Secondly, the image is represented by a content-sensitive hypergraph. Instead of using fixed features and parameters for all the images, we propose a new content-sensitive method for feature selection and hypergraph construction. In this method, the most discriminant color channel which maximizes the difference between p-ROI and the background is selected for each image. Also the number of neighbors in hyperedges is adjusted automatically according to the image content. Finally, an incremental hypergraph partitioning is utilized to generate the candidate regions for the final salient object detection, in which all the candidate regions are evaluated by p-ROI and the best match one will be the selected as final salient object. Our approach has been extensively evaluated on a large benchmark image database. Experimental results show that our approach can not only achieve considerable improvement in terms of commonly adopted performance measures in salient object detection, but also provide more precise object boundaries which is desirable for further image processing and understanding.     

融合深度特征和多核增强学习的显著目标检测

目的 针对现有基于手工特征的显著目标检测算法对于显著性物体尺寸较大、背景杂乱以及多显著目标的复杂图像尚不能有效抑制无关背景区域且完整均匀高亮显著目标的问题,提出了一种利用深度语义信息和多核增强学习的显著目标检测算法。方法 首先对输入图像进行多尺度超像素分割计算,利用基于流形排序的算法构建弱显著性图。其次,利用已训练的经典卷积神经网络对多尺度序列图像提取蕴含语义信息的深度特征,结合弱显著性图从多尺度序列图像内获得可靠的训练样本集合,采用多核增强学习方法得到强显著性检测模型。然后,将该强显著性检测模型应用于多尺度序列图像的所有测试样本中,线性加权融合多尺度的检测结果得到区域级的强显著性图。最后,根据像素间的位置和颜色信息对强显著性图进行像素级的更新,以进一步提高显著图的准确性。结果 在常用的MSRA5K、ECSSD和SOD数据集上与9种主流且相关的算法就准确率、查全率、F-measure值、准确率—召回率（PR）曲线、加权F-measure值和覆盖率（OR）值等指标和直观的视觉检测效果进行了比较。相较于性能第2的非端到端深度神经网络模型,本文算法在3个数据集上的平均F-measure值、加权F-measure值、OR值和平均误差（MAE）值,分别提高了1.6%,22.1%,5.6%和22.9%。结论 相较于基于手工特征的显著性检测算法,本文算法利用图像蕴含的语义信息并结合多个单核支持向量机（SVM）分类器组成强分类器,在复杂图像上取得了较好的检测效果。     

Salient object detection: From pixels to segments

In this paper we propose a novel approach to the task of salient object detection. In contrast to previous salient object detectors that are based on a spotlight attention theory, we follow an object-based attention theory and incorporate the notion of an object directly into our saliency measurements. Particularly, we consider proto-objects as units of the analysis, where a proto-object is a connected image region that can be converted into a plausible object or object-part, once a focus of attention reaches it. As the object-based attention theory suggests, we start with segmenting a complex image into proto-objects and then assess saliency for each proto-object. The most salient proto-object is considered as being a salient object.     

Salient object detection using task simulation as a new input

Multimedia Tools and Applications - Saliency or the salient region changes in the human vision system depending on the type of its behavior and task. That is, the salient region in human vision...     

Salient object detection based on regions

Salient object detection aims to automatically localize the attractive objects with respect to surrounding background in an image. It can be applied to image browsing, image cropping, image compression, content-based image retrieval, and etc. In the literature, the low-level (pixel-based) features (e.g., color and gradient) were usually adopted for modeling and computing visual attention; these methods are straightforward and efficient but limited by performance, due to losing global organization and inference. Some recent works attempt to use the region-based features but often lead to incomplete object detection. In this paper, we propose an efficient approach of salient object detection using region-based representation, in which two novel region-based features are extracted for proposing salient map and the salient object are localized with a region growing algorithm. Its brief procedure includes: 1) image segmentation to get disjoint regions with characteristic consistency; 2) region clustering; 3) computation of the region-based center-surround feature and color-distribution feature; 4) combination of the two features to propose the saliency map; 5) region growing for detecting salient object. In the experiments, we evaluate our method with the public dataset provided by Microsoft Research Asia. The experimental results show that the new approach outperforms other four state-of-the-arts methods with regard to precision, recall and F-measure.     

Salient object detection: A survey

Computational Visual Media - Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many...     

基于蚁群优化算法的弱光图像显著性目标检测

近年来,显著性目标检测受到工业界和学术界的大量关注,成为了计算机视觉领域中一项重要的基础研究,该问题的解决有助于各类视觉任务取得突破性进展。尽管针对可见光场景的显著性检测工作已经取得了有效成果,但如何在信噪比偏低、可用有效信息匮乏的弱光图像中提取边界清晰、内部结构准确的显著性目标,仍然是具有挑战性的难题。针对弱光场景下显著性目标检测存在边界模糊、结构不完整等造成准确率较低的问题,提出基于蚁群优化（ACO）算法的显著性检测模型。首先,通过多尺度超像素分割将输入图像转换为具有不同节点的无向图;其次,基于最优特征选择策略来更充分地获取低对比度弱光图像中所包含的更多显著目标的特征信息,并摒弃冗余的噪声信息;然后,引入空间对比度策略用于探索弱光图像中具有相对较高对比度的全局显著性线索。而为了在低信噪比情况下也能获取准确的显著性估计,利用ACO算法对显著图进行优化。通过在3个公共数据集（MSRA、CSSD和PASCAL-S）以及夜间弱光图像（NI）数据集上进行实验,可以看出,所提模型在3个公共数据集上的曲线下面积（AUC）值分别达到了87.47%、84.27%和81.58%,在NI数据集上的AUC值比排名第2的低秩矩阵恢复（LR）模型提高了2.17个百分点。实验结果表明,相较于11种主流的显著性检测模型,所提模型具有结构更准确且边界更清晰的检测效果,有效抑制了弱光场景对显著性目标检测性能的干扰。     

Salient object detection via robust dictionary representation

Multimedia Tools and Applications - The theory of sparse and low-rank representation has worked competitive performance in the field of salient object detection. Generally, the salient object is...     

基于背景学习的显著物体检测

目的 显著物体检测的目标是提取给定图像中最能吸引人注意的物体或区域,在物体识别、图像显示、物体分割、目标检测等诸多计算机视觉领域中都有广泛应用。已有的基于局部或者全局对比度的显著物体检测方法在处理内容复杂的图像时,容易造成检测失败,其主要原因可以总结为对比度参考区域设置的不合理。为提高显著物体检测的完整性,提出背景驱动的显著物体检测算法,在显著值估计和优化中充分利用背景先验。方法 首先采用卷积神经网络学习图像的背景分布,然后从得到的背景图中分割出背景区域作为对比度计算参考区域来估计区域显著值。最后,为提高区域显著值的一致性,采用基于增强图模型的优化实现区域显著值的扩散,即在传统k-正则图局部连接的基础上,添加与虚拟节点之间的先验连接和背景区域节点之间的非局部连接,实现背景先验信息的嵌入。结果 在公开的ASD、SED、SOD和THUS-10000数据库上进行实验验证,并与9种流行的算法进行对比。本文算法在4个数据库上的平均准确率、查全率、F-measure和MAE指标分别为0.873 6、0.795 2、0.844 1和0.112 2,均优于当前流行的算法。结论 以背景区域作为对比度计算参考区域可以明显提高前景区域的显著值。卷积神经网络可以有效学习图像的背景分布并分割出背景区域。基于增强图模型的优化可以进一步实现显著值在前景和背景区域的扩散,提高区域显著值的一致性,并抑制背景区域的显著性响应。实验结果表明,本文算法能够准确、完整地检测图像的显著区域,适用于复杂图像的显著物体检测或物体分割应用。     

Salient object detection via multiple saliency weights

Multimedia Tools and Applications - Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects...     

Salient object detection using color spatial distribution and minimum spanning tree weight

Salient object detection is very useful in many computer vision applications such as image segmentation, content-based image editing and object recognition. In this paper, we present a salient object detection algorithm by using color spatial distribution (CSD) and minimum spanning tree weight (MSTW). We first use a segmentation algorithm to decompose an image into superpixel-level elements, then use these elements as nodes to construct a minimum spanning tree (MST), each connected edge weight is the mean color difference between two nodes. CSD of each element can be computed by integrating color, spatial distance and MSTW. Note that if the color of one element is the most widely distributed over the entire image, it should have the biggest CSD value, we regard this element as a background node (BG Node). Then we use the MSTW between other element and BG node to generate a MSTW map. The superpixel-level saliency map can be obtained by combining the CSD map and MSTW map. Finally, we use a guided filter to get the pixel-level saliency map. Experimental results on two databases demonstrate that our proposed method outperforms other previous state-of-the-art approaches.     

Salient object detection via saliency bias and diffusion

Salient object detection aims to identify both spatial locations and scales of the salient object in an image. However, previous saliency detection methods generally fail in detecting the whole objects, especially when the salient objects are actually composed of heterogeneous parts. In this work, we propose a saliency bias and diffusion method to effectively detect the complete spatial support of salient objects. We first introduce a novel saliency-aware feature to bias the objectness detection for saliency detection on a given image and incorporate the saliency clues explicitly in refining the saliency map. Then, we propose a saliency diffusion method to fuse the saliency confidences of different parts from the same object for discovering the whole salient object, which uses the learned visual similarities among object regions to propagate the saliency values across them. Benefiting from such bias and diffusion strategy, the performance of salient object detection is significantly improved, as shown in the comprehensive experimental evaluations on four benchmark data sets, including MSRA-1000, SOD, SED, and THUS-10000.     

Salient object detection and classification for stereoscopic images

Stereoscopic images have become more and more prevalent following the rapid advances in 3D capturing and display techniques. However, there has been little research on visual content analysis for stereoscopic images. In this paper, we address the challenging problem of object detection and classification for stereoscopic images. An iterative method that can mutually boost salient object detection and object classification is proposed for stereoscopic images. This method includes two steps. In the first step, a 3D saliency detection method, which includes the contrastive and occlusion cues contained in each stereoscopic image pair along with the discriminative features provided by the SVM classifier, is proposed to localize object of interest in the stereoscopic images. In the second step, the bag of word features of foreground and background is pooled by using the localization information, and then is applied to train the SVM classifier. Each of the two steps benefits from the gradual improvement result in the other, no matter in the training or the testing process. To evaluate the performance of our approach, a 6-object class dataset of stereoscopic images real objects viewed under general lighting conditions, poses and viewpoints is set up. Our experimental results on the dataset, for object localization and object classification, demonstrate the effectiveness of the method.