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With the widespread of user-generated Internet videos, emotion recognition in those videos attracts increasing research efforts. However, most existing works are based on framelevel visual features and/or audio features, which might fail to model the temporal information, e.g. characteristics accumulated along time. In order to capture video temporal information, in this paper, we propose to analyse...
People drive on the road and eat in the kitchen. Can the road imply driving or the kitchen imply eating? This paper addresses such a problem by studying the relations between actions and scenes. To get effective scene representation, we use a deep convolutional neural networks (CNN) model trained from a scene-centric database to predict scene responses for videos. We employ two encoding schemes based...
Action recognition has been one of the challenging problems in the computer vision community. Most of the recent research work in this area exploits the motion features captured by dense trajectory descriptors. On the other hand, static image classification has seen the rise of deep learning architectures, with evidence that the output of intermediate layers could be successfully employed as a low...
Cultivar identification is an important aspect in agriculture and also a typical task of fine-grained visual categorization (FGVC). In comparison with other common topics in FGVC, studies on this problem are somewhat lagged and limited. In this paper, targeting four Chinese maize cultivars of Jundan No.20, Wuyue No.3, Nongda No.108, and Zhengdan No.958, we first consider the problem of identifying...
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In this paper, an approach using the spatio-temporal feature and nonnegative locality-constrained linear coding (NLLC) is proposed to detect abnormal events in videos. This approach utilizes position-based spatio-temporal descriptors as the low-level representations of a video clip. Each descriptor consists of the position information of a space-time interest point and an appearance feature vector...
We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to...
In contrast to still image analysis, motion information offers a powerful means to analyze video. In particular, motion trajectories determined from keypoints have become very popular in recent years for a variety of video analysis tasks, including search, retrieval and classification. Additionally, cloud-based analysis of media content has been gaining momentum, so efficient communication of salient...
With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.M1 classifier. Our proposed DNA...
Incongruity between emotional experience and its outwardly expression is one of the prominent symptoms in schizophrenia. Though widely reported and used in clinical evaluation, this symptom is inadequately defined in the literature and may be confused with mere affect flattening. In this study we used structured-light depth camera and dedicated software to automatically measure facial activity of...
In this paper, we consider a single sensor classification problem, focusing on classifying the types of the moving vehicles. To improve the classification accuracy with low-time complexity in complex scenes, the acoustic sensor data sets were captured to measure the physical events and a novel hybrid dictionary learning method for vehicle classification is proposed. The efficient hybrid dictionary...
Brain-computer interfacing (BCI) based on steady-state visual evoked potentials (SSVEPs) is one of the most practical BCIs because of its high recognition accuracies and little training of a user. Mixed frequency and phase coding which can implement a number of commands and achieve a high information transfer rate (ITR) has recently been gaining much attention. In order to implement mixed-coded SSVEP-BCI...
With the development of surveillance cameras, person re-identification has gained much interest, however re-identifying people across cameras remains a challenging problem which not only requires a good feature description but also a reliable matching scheme. Our method can be applied with any feature and focuses on the second requirement. We propose a robust bidirectional sparse coding method that...
In this paper, we propose a new local descriptor for action recognition in depth images. Our proposed descriptor jointly encodes the shape and motion cues using surface normals in 4D space of depth, time, spatial coordinates and higher-order partial derivatives of depth values along spatial coordinates. In a traditional Bag-of-words (BoW) approach, local descriptors extracted from a depth sequence...
Scene recognition aims to find a semantic explanation of a scene, i.e., it helps intelligent machines to know where they are. It can be widely applied into various tasks in computer vision and robotics. Most of pioneer methods extracted a set of low-level features and put them into classifier directly to identify scene category. But it has been proved that low-level features do not work well. Currently...
Pedestrian detection is one of the challenging research topics in computer vision and efficient feature representation of a pedestrian attracts more and more attention. Traditional features such as Histogram of Oriented Gradients (HOG) were widely used in pedestrian detection, but because of their poor texture description ability, these feature based methods cannot achieve satisfactory pedestrian...
This work introduces a novel feature detection algorithm for the decoding of a binary encoded structured light pattern. To make the structure light pattern insensitive to surface color and texture, some geometrical shapes are used as the pattern elements. Grid-point between each two adjacent rhombic pattern element is defined as the feature points. Affected by the inner structure of pattern element,...
For large-scale image retrieval, high-dimensional image representations derived from pre-trained Convolutional Neural Networks (CNNs) make the retrieval system inefficiency. In this paper, we propose to combine nonlinear dimension reduction and hashing method for efficient image retrieval. We firstly extract 4096-dimension features by a pre-trained CNNs model. Secondly, we use t-Distributed Stochastic...
Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human...
In this paper, we demonstrate nonlinear features extracted by deep neural network have better results in the task of dictionary learning. A nonlinear dictionary learning model is constructed and the optimization algorithm is developed. In the learning algorithm, we use the deep neural network to convey raw samples to feature space and learn a nonlinear dictionary. The extensive experimental results...
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