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...     

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...     

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 biogeography-based optimization to combine features

In recent studies, ontology related concepts have been introduced into FIPA ACL content language to convey information for agent communication. However, these works have only applied ontology-based knowledge representation in communication message and then demonstrated the advantage of this association. In fact, although ontology can represent semantic implications needed for decidable reasoning support, it has no mechanism for defining complex rule-based representation to support inference. The motivation of this study is to address this issue by developing a semantic-based infrastructure to integrate Semantic Web technologies into ACL message contents. This semantic-based infrastructure defines two different semantic frameworks: the three-tier knowledge representation framework for message content and the Multi-layer Ontology Architecture for content language. The former is developed based on Semantic Web stack to support ontology-based reasoning and rule-based inference. The latter is adopted to develop a Lightweight Ontology-based Content Language (LOCL) to describe agent communication messages in an unambiguous and computer-interpretable way Jena reasoner is used in an application scenario that exploits agent communication with LOCL as content language, OWL as ontology language, and SWRL as rule language to demonstrate the feasibility of the proposed infrastructure.     

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 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: 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...     

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 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 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.     

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 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: 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.     

超图多级划分算法框架及对划分结果的多阶段优化

超图划分应用于大规模矩阵计算、大规模集成电路等领域.详细地阐述了超图多级划分的算法框架,并提出对划分结果进行优化的一种手段,通过进行多阶段的循环优化,在可以接受的运行时间内得到对超图的一个较优的划分.     

Salient object detection employing regional principal color and texture cues

Multimedia Tools and Applications - Saliency in a scene describes those facets of any stimulus that makes it stand out from the masses. Saliency detection has attracted numerous algorithms in...     

基于TI-DMC642的显著性物体检测

基于硬件平台的显著性物体检测技术应用到生产生活中,便可以通过便携设备,实现诸如盲人的视觉导航、交通系统中的路牌识别等问题,具有很高的实用价值.基于TI-DMC642平台,采用谱残差的方法,解决图像分辨率调整、IFFT编码、滤波和阈值化等问题后,可以最终得到效果不错的显著性物体检测并实现在PC机上很难做到的动态实时显示.     

Salient object detection via local saliency estimation and global homogeneity refinement

This paper presents a new hybrid approach for detecting salient objects in an image. It consists of two processes: local saliency estimation and global-homogeneity refinement. We model the salient object detection problem as a region growing and competition process by propagating the influence of foreground and background seed-patches. First, the initial local saliency of each image patch is measured by fusing local contrasts with spatial priors, thereby the seed-patches of foreground and background are constructed. Later, the global-homogeneous information is utilized to refine the saliency results by evaluating the ratio of the foreground and background likelihoods propagated from the seed-patches. Despite the idea is simple, our method can effectively achieve consistent performance for detecting object saliency. The experimental results demonstrate that our proposed method can accomplish remarkable precision and recall rates with good computational efficiency.