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Video summarization is an important multimedia task for applications such as video indexing and retrieval, video surveillance, human-computer interaction and video "storyboarding". In this paper, we present a new approach for automatic summarization of video collections that leverages a structured minimum-risk classifier and efficient submodular inference. To test the accuracy of the predicted...
Class imbalance exists in many applications of bioinformatics and biomedicine, while dimension reduction in the feature space is often needed when building prediction models on a dataset. When the above two issues need to be considered simultaneously for skewed/imbalanced datasets, practitioners and researchers in machine learning may raise the following question: should feature selection be conducted...
In this letter, a novel no reference image quality metric is developed, a set of ten features are extracted from each distorted image, then Relevance Vector Machine algorithm (RVM) is utilized to learn the mapping between the combined features and human opinion scores, experiments are conducted on the LIVE databases. The performance of the proposed metric is compared with some existing NR metrics...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
This work focuses on cost reduction methods, applied on forest species recognition systems as a case-study. Current state-of-the-art shows that the accuracy of these systems, generally employing texture recognition approaches, have increased considerably in the past years. However, the cost in time to perform the recognition of input samples has also increased proportionally. By taking into account...
In recent years, network representation learning (NRL) has been increasingly applied into web data analysis, such as video, image and text. Most of NRL methods can widely pursue nodes classification, community detection and link prediction tasks. Due to the nodes in these kinds of networks mostly contain the common attributes and share the same neighbors, we identify them as homogeneous networks,...
Many existing person re-identification (PRID) methods typically attempt to train a faithful global metric offline to cover the enormous visual appearance variations, so as to directly use it online on various probes for identity match- ing. However, their need for a huge set of positive training pairs is very demanding in practice. In contrast to these methods, this paper advocates a different paradigm:...
Over a decade of continual expansion in networking and cloud computing has naturally created an increased demand for cybersecurity solutions. Due to the large number of communication devices and content, it is ideal that these cybersecurity solutions are automated. Unfortunately, malicious content and/or activity is often designed to “look” normal and new malicious attacks are repeatedly being developed...
Existing work on identifying security requirements relies on training binary classification models using domain-specific data sets to achieve a high accuracy. Considering that domain-specific data sets are often not readily available, we propose a domain-independent model for classifying security requirements based on two key ideas. First, we train our model on the description of weaknesses from the...
Recent works on crowd counting have achieved promising performance by employing the Convolutional Neurol Network (CNN) based features. These works usually design a deep network to detect pedestrian heads, and then count them. In this paper, we propose a novel approach to count pedestrians effectively based on the statistical CNN features. In particular, our approach only uses the first layer features...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
Face recognition-based authentication techniques can be easily spoofed using various types of attack. Consistent counter-measures need to meet certain requirements, mainly regarding reliable robustness and low complexity. In this paper, we aim to find the best compromise between these two criteria, as we propose an anti-spoofing solution based on Image Quality Assessment (IQA) to distinguish between...
High dropout rate of MOOC is criticized while a dramatically increasing number of learners are appealed to these online learning platforms. Various works have been done on analysis and prediction of dropout. Machine learning techniques are widely applied to this field. However, a single classifier may not always perform reliable for predictions. In this work, we study dropout prediction for MOOC....
Deployment of Network Function Virtualization (NFV) over multiple clouds accentuates its advantages like flexibility of virtualization, proximity to customers and lower total cost of operation. However, NFV over multiple clouds has not yet attained the level of performance to be a viable replacement for traditional networks. One of the reasons is the absence of a standard based Fault, Configuration,...
People tend to read multiple news articles on a topic since a single article may not contain all important information. A summary of all the articles related to topic will save the time and energy. Text Summarization is a way of minimizing a textual document to a meaningful summary. In this research, an extractive-based approach is used to generate a two-level summary from online news articles. News...
Multimodal classification arises in many computer vision tasks such as object classification and image retrieval. The idea is to utilize multiple sources (modalities) measuring the same instance to improve the overall performance compared to using a single source (modality). The varying characteristics exhibited by multiple modalities make it necessary to simultaneously learn the corresponding metrics...
This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images. Because existing methods often use the difference of image intensities between a matching pair (e.g., the absolute difference of gait energies for the l1-norm) to measure a dissimilarity, large intrasubject differences derived from...
Kinship verification from facial images is a challenging task in computer vision. The majority of recent verification algorithms concatenate all features of patches in facial image to build the final feature representation, which implicitly takes every facial part into account for kinship verification. However, it is questionable by considering all face regions since certain facial parts such as the...
The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. This paper proposes an ensemble learning technique which combines the efforts of multiple metric learning algorithms like Large Margin Nearest Neighbours...
Breast cancer is one of the most common cancer in women worldwide. It is typically diagnosed via histopathological microscopy imaging, for which image analysis can aid physicians for more effective diagnosis. Given a large variability in tissue appearance, to better capture discriminative traits, images can be acquired at different optical magnifications. In this paper, we propose an approach which...
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