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Dynamic selection (DS) is a mechanism to select one or an ensemble of competent classifiers from a pool of base classifiers, in order to classify a specific test sample. The size of this pool is user defined and yet crucial to control the computational complexity and performance of a DS. An appropriate pool size depends on the choice of base classifiers, the underlying DS method used, and more importantly,...
Image quality assessment gains a greater interest due to development of digital imaging and storage. In that field, structural similarity (SSIM) index has been shown to favorably agree with human perceptual assessment, significantly outperforming the method of mean squared error, i.e., L2 distance. The similarity measure function in SSIM which compares a target (distorted) image with its reference...
Multiple kernel learning methods combine a set of base kernels to produce an optimal one for a certain classification or regression problem. But selecting a set of base kernels from a plethora of kernels is not automated. We provide a criteria to select efficient base kernels. Automating the selection process of efficient base kernel requires less time and effort than manually selecting them. However,...
This paper addresses the problem of determining whether an observed subject has already been seen in a stream of biometric samples. Given a new sample, unlike the common practice of comparing a related match score to a constant threshold, this work introduces a function which takes as input the match score and the position of that sample in the stream, and produces as output a duplicate/non-duplicate...
Cocaine dependence devastates millions of human lives. Despite of a variety of treatments, there is a very high rate of individual relapse to drug use. In the last decade, functional magnetic resonance imaging (fMRI) proved to be a powerful tool to diagnose and understand different pathologies. This work provides advances in the identification of cocaine dependence and in the relapse prediction based...
A comprehensive framework for detection and characterization of partial intrinsic symmetry over 3D shapes is proposed. To identify prominent symmetric regions which overlap in space and vary in form, the proposed framework is decoupled into a Correspondence Space Voting (CSV) procedure followed by a Transformation Space Mapping (TSM) procedure. In the CSV procedure, significant symmetries are first...
This paper explores the supervised pattern recognition problem based on feature partitioning. This formulation leads to a new problem in computational geometry. The supervised pattern recognition problem is formulated as an heuristic good clique cover problem satisfying the k-nearest neighbors rule. First it is applied a heuristic algorithm for partitioning a graph into a minimal number of cliques...
Multi-label classification has attracted many attentions in various fields, such as text categorization and semantic image annotation. Aiming to classify an instance into multiple labels, various multi-label classification methods have been proposed. However, the existing methods typically build models in the identical feature (sub)space for all labels, possibly inconsistent with real-world problems...
Multiverse networks were recently proposed as a method for promoting more effective transfer learning. While an extensive analysis was proposed, this analysis failed to capture two main aspects of these networks: (i) the rank of the representation is much lower than the rank predicted by the analysis; and (ii) the contribution of increased multiplicity in such networks diminishes quickly. In this...
our task of video copy detection system aims to locate vicdeo segments that are partially copied or near-duplicated versions from an archive of reference videos. In 2010, video copy detection problem was sometimes considered as a solved problem, since previous research within this area used either small-scale or large-scale datasets (e.g. TRECVID 2009, Muscle-VCD) with pre-defined simulated videos...
A+ aka Adjusted Anchored Neighborhood Regression - is a state-of-the-art method for exemplar-based single image super-resolution with low time complexity at both train and test time. By robustly training a clustered regression model over a low-resolution dictionary, its performance keeps improving with the dictionary size - even when using tens of thousands of regressors. However, this can pose a...
Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly...
In this paper, we present a novel method for constructing a generative model to analyze the structure of labeled data. Given a time-series of sample graphs, we aim to learn a so-called “supergraph” that best describes the underlying average connectivity structure presenting in the data. In this time-series the vertex set is fixed and labeled and the set of possible connections between vertices change...
Efficient detection of three dimensional (3D) objects in point clouds is a challenging problem. Performing 3D descriptor matching or 3D scanning-window search with detector are both time-consuming due to the 3-dimensional complexity. One solution is to project 3D point cloud into 2D images and thus transform the 3D detection problem into 2D space, but projection at multiple viewpoints and rotations...
Multi-label classification (MLC), allowing instances to have multiple labels, has been received a surge of interests in recent years due to its wide range of applications such as image annotation and document tagging. One of simplest ways to solve MLC problems is label-power set method (LP) that regards all possible label subsets as classes. LP validates traditional multi-classification classifiers...
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