Showing papers in "Pattern Recognition in 2017"

TL;DR: A novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements by backpropagating the explanations from the output to the input layer is introduced.

TL;DR: An analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets or CNNs) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors points that fine tuning tends to be the best performing strategy.

TL;DR: In this paper, a deep autoencoder-based approach is proposed to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with high dynamic range.

TL;DR: Enhanced skeleton visualization method encodes spatio-temporal skeletons as visual and motion enhanced color images in a compact yet distinctive manner and consistently achieves the highest accuracies on four datasets, including the largest and most challenging NTU RGB+D dataset for skeleton-based action recognition.

TL;DR: A simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps to extract only expression specific features from a face image and explore the presentation order of the samples during training.

TL;DR: A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.

TL;DR: A Multi-crop Convolutional Neural Network (MC-CNN) is presented to automatically extract nodule salient information by employing a novel multi-crop pooling strategy which crops different regions from convolutional feature maps and then applies max-pooling different times.

TL;DR: A generic computer vision system designed for exploiting trained deep Convolutional Neural Networks as a generic feature extractor and mixing these features with more traditional hand-crafted features is presented, demonstrating the generalizability of the proposed approach.

TL;DR: In this paper, semi-supervised feature selection methods are fully investigated and two taxonomies of these methods are presented based on two different perspectives which represent the hierarchical structure of semi- supervised feature Selection methods.

TL;DR: This paper learns useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gains intuition of the chosen features based on a Deconvolutional Network (DN) approach, and gains insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems.

TL;DR: A large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets.

TL;DR: The experimental results on three challenging depth video datasets demonstrate that the proposed online HAR method using the proposed multi-fused features outperforms the state-of-the-art HAR methods in terms of recognition accuracy.

TL;DR: A novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user is proposed.

TL;DR: The state-of-the-art CCL algorithms presented in the last decade are reviewed, the main strategies and algorithms are explained, their pseudo codes are presented, and experimental results are given in order to bring order of the algorithms.

TL;DR: In this article, a new adaptation layer is proposed to reduce the mismatch between training and test data on a particular source layer, and the adaptation process can be efficiently and effectively implemented in an unsupervised manner.

TL;DR: Experimental results indicate that framework built based on CNN and ELM provides competitive performance with small number of training samples, and the average accuracy of ELM can be improved as high as 30.04%, while performs tens to hundreds of times faster than those state-of-the-art classifiers.

TL;DR: Understanding basic mechanisms of postoperative pain to identify effective treatment strategies may improve patients' outcome after surgery and point towards useful elements of multimodal analgesia able to reduce opioid consumption, improve pain management, and enhance recovery.

TL;DR: A k -nearest neighbors-based synthetic minority oversampling algorithm, termed SMOM, to handle multiclass imbalance problems, which can aggressively explore the regions of minority classes by configuring a high value for parameter k, but do not result in severe over generalization.

TL;DR: Zhang et al. as discussed by the authors proposed a hybrid deep architecture which combines Fisher vectors and deep neural networks to learn non-linear transformations of pedestrian images to a deep space where data can be linearly separable.

TL;DR: A novel region-based model for the segmentation of objects or structures in images is proposed by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentations results.

TL;DR: A widely used statistical body representation from the largest commercially available scan database is rebuilt, and the resulting model is made available to the community by developing robust best practice solutions for scan alignment that quantitatively lead to the best learned models.

TL;DR: The results show that joining CNNs and adaptive gradient methods leads to the state-of-the-art in unconstrained head pose estimation.

TL;DR: Watch, Attend and Parse (WAP), a novel end-to-end approach based on neural network that learns to recognize HMEs in a two-dimensional layout and outputs them as one-dimensional character sequences in LaTeX format, significantly outperformed the state-of-the-art method.

TL;DR: In this approach, a wavelet constrained pooling layer is designed to replace the conventional pooling in CNN and the new architecture can suppress the noise and is better at keeping the structures of the learned features, which are crucial to the segmentation tasks.

TL;DR: A novel approach called joint sparse principal component analysis (JSPCA) is proposed to jointly select useful features and enhance robustness to outliers and the experimental results demonstrate that the proposed approach is feasible and effective.

TL;DR: A multi-modality classification framework to efficiently exploit the complementarity in theMulti-modal data is presented and pairwise similarity is calculated for each modality individually using the features including regional MRI volumes, voxel-based FDG-PET signal intensities, CSF biomarker measures, and categorical genetic information.

TL;DR: Experimental results on benchmark datasets for unsupervised feature selection show that SCUFS outperforms the state-of-the-art UFS methods and can uncover the underlying multi-subspace structure of data.

TL;DR: A novel approach, namely CR_CompCode, which can achieve high recognition accuracy while having an extremely low computational complexity is proposed, which is highly effective and efficient for contactless palmprint identification.

TL;DR: This survey investigates fourty-four studies on image-based insect recognition and tries to give a global picture on what are the scientific locks and how the problem was addressed.

TL;DR: A general framework for multi-biometric template protection based on homomorphic probabilistic encryption, where only encrypted data is handled, showing that all requirements described in the ISO/IEC 24745 standard on biometric data protection are met with no accuracy degradation.