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Numbers of samples in different classes are in nature imbalanced in many machine learning problems. Single classifier-based methods are subject to high variance. Therefore, ensemble-based methods are more suitable for dealing with imbalanced pattern classification problems. In this work, we propose a boosting-based method: BSMBoost which creates an ensemble of classifiers using samples selected by...
Stacked auto-encoder is mainly used for image classification and it can extract valid information from data through unsupervised pre-training and supervised fine-tuning. This paper is intended to improve the accuracy of image classification, we constructed a 6-layer stacked convolution neural network (CNN) based on stacked auto-encoders. The constructed CNN can extract effective features for image...
In the realm of surface electromyography (sEMG) gesture recognition, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for them to train on. Consequently, it would be prohibitively time consuming for a single user to generate a sufficient amount of data for training such algorithms. In this paper, two datasets of 18 and 17 able-bodied participants...
There has been a phenomenal increase in the utility of text classification (TC) in applications like targeted advertisement and sentiment analysis. Most applications demand that the model be efficient and robust, yet produce accurate categorizations. This is quite challenging as their is a dearth of labelled training data because it requires assigning labels after reading the whole document. Secondly,...
To successfully increase athletes' or exercisers' fitness and endurance, the factors of physiological signal, emotion, or the level of fatigue should be considered during the training program. Many clinical decision support systems can assist to monitor the exercisers by some wearable devices. And, the questionnaire should also be taken into account to produce a report. Such process is cumbersome,...
Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this...
This paper presents a multiple classifier system (MCS) to identify plants species based on the texture and shape features extracted from leaf images. A diverse pool of SVM and Neural Network classifiers is trained on four different feature sets, namely, Local Binary Pattern (LBP), Histogram of Gradients (HOG), Speed of Robust Features (SURF) and Zernike Moments (ZM). Then, a static classifier selection...
Beyond providing alternatives to build rubrics, a group of researchers in Tecnológico de Costa Rica found the need to implement best practices in the definition and validation of evaluation rubrics, to encourage the creation of a Higher Education bank of assessment instruments. To achieve this goal, in the Tecnológico de Costa Rica, a teacher training process was established, which includes cooperative-collaborative...
In this paper, the problem of adaptive beamforming in the presence of direction-of-arrival (DOA) mismatch is investigated. To develop a robust beamformer against such an imperfection, a new approach is devised by formulating an output signal-to-interference-plus-noise ratio (SINR) maximization problem. In particular, the proposed robust beamforming approach consists of two steps. At first, the standard...
Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to...
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,...
Despite great progress has been made in recent years, efficient and robust people detection continues to be a challenging problem in the filed of computer vision. In this paper, we propose a highly efficient indoor people detect method based on RGB-D sensor. First, two RGB and depth feature fusing strategies are proposed and compared. Secondly, an improved non-maximum suppression algorithm is proposed...
The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images. While deep metric learning has yielded impressive performance gains by extracting high level abstractions from image data, a proper objective loss function becomes the central issue to boost the performance. In this paper,...
We introduce a novel method for 3D object detection and pose estimation from color images only. We first use segmentation to detect the objects of interest in 2D even in presence of partial occlusions and cluttered background. By contrast with recent patch-based methods, we rely on a “holistic” approach: We apply to the detected objects a Convolutional Neural Network (CNN) trained to predict their...
Surface reconstruction from a point cloud is a standard subproblem in many algorithms for dense 3D reconstruction from RGB images or depth maps. Methods, performing only local operations in the vicinity of individual points, are very fast, but reconstructed models typically contain lots of holes. On the other hand, regularized volumetric approaches, formulated as a global optimization, are typically...
We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type I and Type II attacks using several standard...
We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors. The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently “spread-out” over the space. In this work, we propose a regularization term to maximize...
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In...
To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the training convergence of this triplet model can be compromised by the fact that the vast majority of the...
Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each...
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