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In this paper, we propose a new discriminative dictionary learning framework, called robust Label Embedding Projective Dictionary Learning (LE-PDL), for data classification. LE-PDL can learn a discriminative dictionary and the blockdiagonal representations without using the l0-norm or l1-norm sparsity regularization, since the l0 or l1-norm constraint on the coding coefficients used in the existing...
Consider a face image data set from clients of a company and the problem of building a face recognition system from it. Video cameras can be used to acquire several images per client in order to maximize the robustness of the system. However, as the data set grows huge, the accuracy of the system might be seriously compromised since the number of negative samples for each user is increasing. We propose...
This paper proposes efficient and powerful deep networks for action prediction from partially observed videos containing temporally incomplete action executions. Different from after-the-fact action recognition, action prediction task requires action labels to be predicted from these partially observed videos. Our approach exploits abundant sequential context information to enrich the feature representations...
Geographical Information System (GIS) is used to collect, manipulate, analyze, and display the geospatial data. The compilation and management of this spatial data is expensive and time consuming task. Due to rapid growth of distributed networks and Internet, it becomes easy to handle data but at the same time it becomes easy to copy or distribute the spatial data. Therefore copyright protection,...
In this paper, a new heterogeneous neural networks based deep learning method, named HNNDL, is presented for supervised classification of hyperspectral image (HSI) with a small number of labeled samples. Specifically, a deep neural Network (DNN) and a convolutional neural network (CNN) are combined to build a HNNDL architecture. The proposed architecture contains three modules: 1) dimension reduction...
Identifying the physical person behind an SNS account has become a critical issue in investigations of SNS-involved crime cases. It is a challenging task because information provided by users on an SNS platform could be false, conflicting, missing and deceptive. One way to gain an accurate profile of a user is to link up all their multiple accounts created on different social platforms, which is referred...
Pedestrian recognition is one of the key components for assisted and autonomous driving. So far many researchers have investigated systems combining a high density LIDAR with cameras or stereo, which results in an expensive and complex setup where the LIDAR data is mostly used to extract regions of interest for the 2D sensor. Very few work has focused on using pure 3D data coming from the LIDAR to...
The recognition of human activities in the field of video surveillance is attracting more researchers. This has led to various approaches and proposals using different methods and techniques. The growing interest in the surveillance has also led researchers to give importance to abnormal human activities in order to propose appropriate and dedicated techniques to this type of activities. Unfortunately,...
In social network analysis, the fundamental idea behind the notion of roles is to discover actors who have similar structural signatures. Actors performing the same role have similar behavioural and functional characteristics. Few examples of structural roles are bridge nodes, clique members and star centers. Role discovery involves partitioning the nodes in a network based on their structural characteristics...
Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named...
Text data present in scene images may be the important clue for indexing, automatic footnote, and indexing of images. Now-a-days extraction of text from images has become one of the fastest growing research areas in the field of computer vision. In scene images, text data are present with huge variations in font sizes, styles, alignments, and orientations. These variations make the task of detection...
Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the view of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l2,1-norm and the non-negative constraints not only removes the irrelevant...
High-throughput technologies have enabled us to rapidly accumulate a wealth of diverse data types. These multi-view data contain much more information to uncover the cluster structure than single-view data, which draws raising attention in data mining and machine learning areas. On one hand, many features are extracted to provide enough information for better representations, on the other hand, such...
Outlier detection is a key technique in data ming and machine learning fields. The deviating characters of outliers make huge detrimental effects on the learning tasks. A lot of algorithms are therefore proposed to handle outliers from different perspectives, such as distance, density, angle and so on. Among these approaches, the density-based methods achieve better performance, but also suffer from...
In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature extraction from time series data that did not require...
In this paper, we aim to address the issue that semi-supervised learning is prone to be influenced by the quality and quantity of initial seeds. In order to expand the initial labeled data, we select credible samples from unlabeled data by a proposed bilateral latent information miner. The miner can extract information from unlabeled data for both positive and negative class respectively. Then we...
A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify...
People count is an important indicator in video surveillance. Due to the overlapping objects and cluttered background, counting people accurately in actual crowded scene remains a non-trivial problem. Existing regression-based methods either learn a single model mapping the global feature to people count, or estimate localized count by training a large number of regressors. In this paper, we present...
During the past years, malicious PDF files have become a serious threat for the security of modern computer systems. They are characterized by a complex structure and their variety is considerably high. Several solutions have been academically developed to mitigate such attacks. However, they leveraged on information that were extracted from either only the structure or the content of the PDF file...
This paper focuses on the problem of built-up areas detection in single high-resolution SAR images. In consideration of the rich structure information of built-up areas in high-resolution SAR images, we put forward a multiscale CNN model to extract multiscale trained features directly from image patches to detect built-up areas. By processing features extraction and classification as a whole, we overcome...
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