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Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
Recent low-rank based matrix/tensor recovery methods have been widely explored in multispectral images (MSI) denoising. These methods, however, ignore the difference of the intrinsic structure correlation along spatial sparsity, spectral correlation and non-local self-similarity mode. In this paper, we go further by giving a detailed analysis about the rank properties both in matrix and tensor cases,...
We propose a novel online Attentional Recurrent Neural Network (ARNN) model for visual tracking, which exploits the feature maps of Convolutional Neural Network (CNN) inside a bounding box to identify whether this target is the one appeared in previous frames. Attention mechanism is adopted for both different parts of targets and different scales of object features. The former attention model is able...
According to the influence of the stand spatial structure on the crown shape of Chinese fir, a new method of diversified 3D Chinese fir modeling based on spatial structure was proposed. In this study, spatial structure units that were divided by a reasonable method were selected in Chinese fir stands. And the data of spatial structure and crown shape in different units was surveyed. Two parameters...
Correlation filters have recently been popular due to their success in short-term single-object tracking as well as their computational efficiency. Nevertheless, the appearance model of a single correlation filter based tracking algorithm quickly forgets the past poses of the target object due to the updates over time. To overcome this undesired forgetting, our approach is to run trackers with separate...
Sample quality plays an important role in tracking-by-learning strategies, but the reliable online samples are hard to be obtained due to challenges of variational environments. By modeling how human visual interest actively guiding the seek of salient regions and movements in video sequences, in this paper, a compositional tracking strategy is proposed based on an integrated saliency map, which is...
Univariate models of the Natural Scene Statistics (NSS) of perceived digital pictures have been deployed in a wide variety of image and video processing applications. However, much less effort has been made towards understanding, modeling, and using bivariate image NSS. Towards filling this gap, Su et al. developed a closed form correlation model of oriented bandpass natural images applicable to adjacent...
Building natural scene statistic models is a potentially transformative development for a wide variety of visual applications, ranging from the design of faithful image and video quality models to the development of perceptually optimized image enhancing techniques. Most predominant statistical models of natural images only characterize the univariate distributions of divisively normalized bandpass...
In this paper, we propose to predict human fixations by incorporating both audio and visual cues. Traditional visual attention models generally make the utmost of stimuli's visual features, while discarding all audio information. But in the real world, we human beings not only direct our gaze according to visual saliency but also may be attracted by some salient audio. Psychological experiments show...
Correlation filters for long-term visual object tracking have recently seen great interest. Although they present competitive performance results, there is still a need for improving their tracking capabilities. In this paper, we present a fast scalable solution based on the Kernalized Correlation Filter (KCF) framework. We introduce an adjustable Gaussian window function and a keypoint-based model...
Semantic attributes represent an adequate knowledge that can be easily transferred to other domains where lack of information and training samples exist. However, in the classical object recognition case, where training data is abundant, attribute-based recognition usually results in poor performance compared to methods that used image features directly. We introduce a generic framework that boosts...
The bag-of-visual-words model has been widely utilized for content based image and video retrieval due to its scalability. In this paper, we extend this model for human action video retrieval. We adopt dense trajectory features which are able to achieve the state-of-the-art performance on action recognition, while most of the existing video retrieval methods utilize descriptors of local interest points...
Soft computing and computing with words (CWW) operate with uncertain natural language (NL) statements to make NL statements more exact. Currently these NL statements in CWW are limited by quantitative concepts. This paper expands CWW beyond quantitative words to texts that contain incongruities. Incongruities are common in jokes, ironies and contradictory texts. A dynamic model, an algorithm, and...
Modeling of visual attention is a very active research domain which has attracted the attention of researchers over the past years. Several models of saliency detection are now available that have shown successful applications in various fields. In this paper, we initially present three models already existing in the literature, we are talking about Itti's model, Imamoglu's model and Le Moans's model...
Automatic assessment of human personality traits is a non-trivial problem, especially when perception is marked over a fairly short duration of time. In this study, thin slices of behavioral data are analyzed. Perceived physical and behavioral traits are assessed by external observers (raters). Along with the big-five personality trait model, four new traits are introduced and assessed in this work...
Information retrieval methods represent query results in a ranked, one dimensional list without revealing connections among documents and document groups. We propose a new model of document representation and extend the notion of similarity to consider document length and word synonyms to organize documents into topically relevant groups. Matches to a user query are presented in an intuitive, interactive...
Automatic annotating images by equipment is of great interest as it meets one's common need for retrieving image content. Usually image content description with keywords is regarded as a visual-word correlation process. However, in view of the viewer's psychology, image to words is a kind of cognition process, which depends more on the experience for one to understand what's in an image. In this paper,...
Visual attention is an important function of the human visual system (HVS). In the long term research of visual attention, various computational models have been proposed with encouraging results. However, most of those work were conducted on images with ideal visual quality. In practice, outputs of most visual communication systems contain different levels of artifacts, e.g. noise, blurring, blockiness...
The image memorability consists in the faculty of an image to be recalled after a period of time. Recently, the memorability of an image database was measured and some factors responsible for this memorability were highlighted. In this paper, we investigate the role of visual attention in image memorability around two axis. The first one is experimental and uses results of eye-tracking performed on...
For the task of image annotation, traditional methods based on probabilistic topic model, such as correspondence Latent Dirichlet Allocation (corrLDA) [1], assumes that image is a mixture of latent topics. However, this kind of models is unable to directly model correlation between topics since topic proportions of an image are generated independently. Our model, called correspondence Correlated Topic...
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