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There is growing interest in social image classification because of its importance in web-based image application. Though there are many approaches on image classification, it is a great problem to integrate multi-modal content of social images simultaneously for social image classification, since the textual content and visual content are represented in two heterogeneous feature spaces. In this study,...
Visual target tracking is one of fundamental research of computer vision field and play an important role in the surveillance application, but it is also one of the difficulties due to the instability of the tracking scene. In this paper, we analyze the major drawbacks of the original Kernelized Correlation Filter (KCF) tracker which causes tracking failure when target experience complicated scenarios...
Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural...
Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model...
The signal or clutter separation in airborne MIMO SAR by the information of the direction-of-departure (DOD) or the direction-of-arrival (DOA) is able to be of benefit to the suppression of the “unwanted” signals. As the signal separation performance after using the blocking matrix algorithm is greatly affected by the estimate deviation of the direction. Moreover, the direction of the “unwanted” signal...
Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label...
The kernel minimum noise fraction (KMNF) method is a nonlinear dimensionality reduction method for hyperspectral images. KMNF can transform the original data into higher dimensional feature space by using nonlinear transformation project. The key issue of KMNF is the noise estimation. The original KMNF performs noises estimation based on spatial neighborhood information. However, the spatial resolution...
In this paper, we proposed an incremental kernel non-negative matrix factorization (IKNMF) to reduce the computing scale in hyperspectral unmixing. Kernel non-negative matrix factorization (KNMF) is an extended non-negative matrix factorization (NMF) able to capture nonlinear dependency features in data matrix through kernel functions. In KNMF algorithm, the size of kernel matrices is closely associated...
Correlation filter-based trackers achieve very good performance in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the image sequence. To solve this problem, we propose a novel and robust scale adaptive tracker combined with color attributes in correlation filter framework, which extracts not only gray but also color information as the feature...
Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose...
In this paper, we propose a robust visual tracking method based on a temporal ensemble framework. Different from conventional ensemble-based trackers, which combine weak classifiers into a strong one using AdBoost in spatial fusion manners, our method adopts a powerful and efficient tracker integrated with its snapshots in different temporal windows of online tracking process to construct a temporal...
Cross-modal hashing has received more and more attention because of its fast query speed and low storage cost. In this paper, we propose a flexible yet simple cross-modal hashing method to deal with the problem of cross-modal retrieval. The proposed method consists of two steps. In the first phase, we use a kernel canonical correlation analysis method named Anchor kernel canonical correlation analysis...
Depth map provides rich information and it can be utilized in object tracking to handle some challenging problems in conventional RGB tracking such as occlusions and model drift. In this paper, we present a tracker that provides an effective real-time target tracking method based on a binocular camera. The proposed tracker is an extension of the popular KCF algorithm that leverages a circulant structure...
Locality-based feature learning has drawn more and more attentions recently. However, most of locality-based feature learning methods only consider a kind of local neighbor information, and such the locality-based methods are difficult to well reveal intrinsic geometrical structure of raw high-dimensional data. In this paper, we propose a novel multi-locality correlation feature learning algorithm...
The Beck Depression Inventory (BDI), a self-report questionnaire consisting of 21 question items, has been the most extensively used for depression assessment. The problem of interest here is to identify a subset of questions in the BDI that are most predictive of depression and can reveal gender differences between depression profiles. We investigate feature selection techniques to select a subset...
Telemetry data, containing the data of multiple subsystems such as power system, implies the on-orbit operation status information of the satellite. We can obtain performance characteristics and fault symptom of the satellite subsystems through analyzing these data. Using classification algorithm we can provide normal data for anomaly detection and find the data from various subsystems which have...
Recently, an increasing number of tone-mapping operators (TMOs) have been proposed in order to display high dynamic nge (HDR) images on low dynamic range (LDR) devices. Developing perceptually consistent image quality assessment (QA) measures for TMO is highly desired because traditional LDR based IQA methods cannot support the cross dynamic range quality comparison. In this paper, a novel objective...
The real-time information on the Web changes dynamically and surge quickly, which cause considerable difficulty in access to interested information. How to mine hot events, how to analyze the correlation of events and how to organize information structurally are challenging tasks. In this paper, to address these problems, we propose STeller, an approach to mine context-aware story — a series of correlated...
The European Space Agency mission Soil Moisture and Ocean Salinity (SMOS) is devoted, since its launch in 2009, to provide global soil moisture and sea surface salinity values. SMOS uses an L band 2-D interferometric radiometer by aperture synthesis to obtain polarimetric brightness temperature images. This work is devoted to compute the spatial correlations in the measured antenna brightness temperature...
In this paper, we present noise-robust photoplethysmographic (PPG) based biometric authentication method for wireless body area networks and m-health applications. The method consists of four steps: (i) preprocessing of PPG signals, (ii) systolic peak detection, (iii) ensemble averaged pulsatile waveform extraction and (iv) pulsatile waveform similarity matching using a normalized cross correlation...
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