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Given the soaring amount of data being generated daily, graph mining tasks are becoming increasingly challenging, leading to tremendous demand for summarization techniques. Feature selection is a representative approach that simplifies a dataset by choosing features that are relevant to a specific task, such as classification, prediction, and anomaly detection. Although it can be viewed as a way to...
Video scene detection, the task of temporally dividing a video into its semantic sections, is an important process for effective analysis of heterogeneous video content. With the increased amount of video available for consumption, video scene detection becomes more and more important by providing means for effective video summarization, search and retrieval, browsing, and video understanding. We...
In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation. Previous works have focused on learning feature embeddings based on metric learning with triplet comparisons and rely only on the qualitative distinction of similar and dissimilar pose labels. In contrast, we consider the exact pose differences between the training samples,...
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...
A key challenge of facial expression recognition (FER) is to develop effective representations to balance the complex distribution of intra- and inter- class variations. The latest deep convolutional networks proposed for FER are trained by penalizing the misclassification of images via the softmax loss. In this paper, we show that better FER performance can be achieved by combining the deep metric...
We introduce a method that uses a single camera to localize a vehicle within a pre-constructed map consisting of a voxel occupancy grid and road-line marker positions. Sophisticated mapping hardware is capable of creating high-accuracy 3D maps of road environments, but localizing a vehicle within such maps is one of the challenges at the forefront of automated driving. A solution which is robust to...
Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations...
Web browsing is an activity that billions of mobile users perform on a daily basis. Battery life is a primary concern to many mobile users who often find their phone has died at most inconvenient times. The heterogeneous multi-core architecture is a solution for energy-efficient processing. However, the current mobile web browsers rely on the operating system to exploit the underlying hardware, which...
As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between...
Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. On the other hand,...
In this paper, we propose a pose-robust metric learning framework for unconstrained face verification by jointly optimizing face and pose verification tasks. We learn a joint model for these two tasks and explicitly discourage the information sharing between pose and identity verification metrics so as to mitigate the information contained in the pose verification task leading to making the identity...
As the state-of-the-art ConvNet-based image retrieval method, spatial search has shown excellent retrieval performance and outperformed other competitors. A key component of this method is a weighted combination of distances evaluated at different regions of a query image. However, these weights are currently manually tuned, by a trial-and-error based exhaustive search. This not only incurs a lengthy...
Malware data are typically depicted with extremely high-dimensional features, which lays an excessive computational burden on detection methods. For the sake of effectiveness and efficiency, feature selection is an indispensable part for malware detection. In this paper, we propose an ensemble feature selection method with integration of discriminative and representative properties for malware detection...
The problem of service composition with end-to-end QoS constraints has been proven to be an NP-hard problem and various evolutionary algorithms have been successfully applied to look for approximately optimal solutions within limited computation time. Favorable heuristic rules are considered as the key of such algorithms, and historical service usage data are widely utilized to help identify the distinct...
We study Automatic Target Recognition (ATR) in infrared (IR) imagery from the perspective of feature fusion. The key to feature fusion is to take advantage of the discriminative and complementary information from different feature sets, which can be represented as internal (within each feature set) or external structures (across different feature sets). Traditional approaches tend to preserve either...
This paper presents a Large Margin Coupled Feature Learning (LMCFL) method for cross-modal face recognition, which recognizes persons from facial images captured from different modalities. Most previous cross-modal face recognition methods utilize hand-crafted feature descriptors for face representation, which require strong prior knowledge to engineer and cannot exploit data-adaptive characteristics...
How to explore complex data? Often, several representations for each data object are available, the data are described by attributes of heterogeneous data type and/or each data object is characterized by many features. It is difficult to choose a suitable similarity measure and an appropriate data mining technique to get an unbiased overview on the information contained in complex data. In this paper,...
The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual similarity between queries and textual representation of software artifacts, which...
Metric learning to learn a good distance metric for distinguishing different people while being insensitive to intra-person variations is widely applied to person re-identification. In previous works, local histograms are densely sampled to extract spatially localized information of each person image. The extracted local histograms are then concatenated into one vector that is used as an input of...
Although multi-view datasets have become more accessible in the real-world applications, most state-of-the-art action recognition methods applied to those datasets rely on simple view agreement when combining local information from various views together. This leads to deteriorated performance in situations with view insufficiency and view disagreements. In this paper, we propose a novel framework...
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