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As the captured information in our real word is very scare and labeling sample is time cost and expensive, semisupervised learning (SSL) has an important application in computer vision and machine learning. Among SSL approaches, a graph-based SSL (GSSL) model has recently attracted much attention for high accuracy. However, for most traditional GSSL methods, the large-scale data bring higher computational complexity, which acquires a better computing platform. In order to dispose of these issues, we propose a novel approach, bipartite GSSL normalized (BGSSL-normalized) method, in this paper. This method consists of three parts. First, the bipartite graph between the original data and the anchor points is constructed, which is parameter-insensitive, scale-invariant, naturally sparse, and simple operation. Then, the label of the original data and anchors can be inferred through the graph. Besides, we extend our algorithm to handle out-of-sample for large-scale data by the inferred label of anchors, which not only retains good classification result but also saves a large amount of time. The computational complexity of BGSSL-normalized can be reduced to $O(ndm+nm^{2})$ , which is a significant improvement compared with traditional GSSL methods that need $O(n^{2}d+n^{3})$ , where $n$ , $d$ , and $m$ are the number of samples, features, and anchors, respectively. The experimental results on several publicly available data sets demonstrate that our approaches can achieve better classification accuracy with less time costs. [ABSTRACT FROM AUTHOR]

Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. Inspired by the effectiveness of recently developed feature learning, we provide a novel deep image saliency computing (DISC) framework for fine-grained image saliency computing. In particular, we model the image saliency from both the coarse-and fine-level observations, and utilize the deep convolutional neural network (CNN) to learn the saliency representation in a progressive manner. In particular, our saliency model is built upon two stacked CNNs. The first CNN generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. Furthermore, we integrate superpixel-based local context information in the first CNN to refine the coarse-level saliency map. Guided by the coarse saliency map, the second CNN focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. For a testing image, the two CNNs collaboratively conduct the saliency computing in one shot. Our DISC framework is capable of uniformly highlighting the objects of interest from complex background while preserving well object details. Extensive experiments on several standard benchmarks suggest that DISC outperforms other state-of-the-art methods and it also generalizes well across data sets without additional training. The executable version of DISC is available online: http://vision.sysu.edu.cn/projects/DISC. [ABSTRACT FROM PUBLISHER]

Visual saliency is one of the most noteworthy perceptual abilities of human vision. Recent progress in cognitive psychology suggests that: 1) visual saliency analysis is mainly completed by the bottom-up mechanism consisting of feedforward low-level processing in primary visual cortex (area V1) and 2) color interacts with spatial cues and is influenced by the neighborhood context, and thus it plays an important role in a visual saliency analysis. From a computational perspective, the most existing saliency modeling approaches exploit multiple independent visual cues, irrespective of their interactions (or are not computed explicitly), and ignore contextual influences induced by neighboring colors. In addition, the use of color is often underestimated in the visual saliency analysis. In this paper, we propose a simple yet effective color saliency model that considers color as the only visual cue and mimics the color processing in V1. Our approach uses region-/boundary-defined color features with spatiochromatic filtering by considering local color-orientation interactions, therefore captures homogeneous color elements, subtle textures within the object and the overall salient object from the color image. To account for color contextual influences, we present a divisive normalization method for chromatic stimuli through the pooling of contrary/complementary color units. We further define a color perceptual metric over the entire scene to produce saliency maps for color regions and color boundaries individually. These maps are finally globally integrated into a one single saliency map. The final saliency map is produced by Gaussian blurring for robustness. We evaluate the proposed method on both synthetic stimuli and several benchmark saliency data sets from the visual saliency analysis to salient object detection. The experimental results demonstrate that the use of color as a unique visual cue achieves competitive results on par with or better than 12 state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

We investigate the tracking of 2-D human poses in a video stream to determine the spatial configuration of body parts in each frame, but this is not a trivial task because people may wear different kinds of clothing and may move very quickly and unpredictably. The technology of pose estimation is typically applied, but it ignores the temporal context and cannot provide smooth, reliable tracking results. Therefore, we develop a tracking and estimation integrated model (TEIM) to fully exploit temporal information by integrating pose estimation with visual tracking. However, joint parsing of multiple articulated parts over time is difficult, because a full model with edges capturing all pairwise relationships within and between frames is loopy and intractable. In previous models, approximate inference was usually resorted to, but it cannot promise good results and the computational cost is large. We overcome these problems by exploring the idea of divide and conquer, which decomposes the full model into two much simpler tractable submodels. In addition, a novel two-step iteration strategy is proposed to efficiently conquer the joint parsing problem. Algorithmically, we design TEIM very carefully so that: 1) it enables pose estimation and visual tracking to compensate for each other to achieve desirable tracking results; 2) it is able to deal with the problem of tracking loss; and 3) it only needs past information and is capable of tracking online. Experiments are conducted on two public data sets in the wild with ground truth layout annotations, and the experimental results indicate the effectiveness of the proposed TEIM framework. [ABSTRACT FROM PUBLISHER]