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This paper deals with gender classification by analyzing footfalls captured by seismic sensor. Gender classification plays an important role in developing different applications like guided navigation system that helps the customer to find different shops in a big shopping mall or to find different departments in a hospital on the basis of their gender. We have tested different classifiers (SVM-Linear,...
The scatter form of multimedia data such as text, image, audio, and video posted regularly in the social media may contain useful information for the organizations. But, this information should be derived with the use of some form of analysis known as Multimodal Sentiment Analysis (MSA). But, there is a lack of proper analytic tools for such analysis. This paper presents a thorough overview of more...
Sparse Representation-based Classifier (SRC) is less sensitive to the shortage of data and the selection of feature space. In this paper, SRC is adopted to perform automatic analysis of tongue substance color and coating color which is considered as small dataset classification task. Firstly, for both training samples and testing samples, the tongue body regions are segmented, the regions of tongue...
This article presents a novel approach to analyze thesoundscape in ecosystems, in order to categorize them in terms of their acoustic properties, focusing on the characterization of four ecosystems through an image classification system which contain information of daily acoustic activity in the frequency range (1kHz-11kHz), for five consecutive months. Emphasis is placed on pre-processing of acoustic...
This paper presents a multi-modal biomedical image classification approach by using a multi-response linear regression (MLR)-based meta-learner as combiner and measure its effectiveness from small to large image collections. The MLR has been proposed as a trainable combiner for fusing class probability outputs of several base-level SVM classifiers on multiple complimentary visual and text features...
In this paper, a novel semantic segmentation model based on aggregated features and contextual information is proposed. Given an RGB-D image, we train a support vector machine (SVM) to predict initial labels using aggregated features, and then optimize the predicted results using contextual information. For aggregated features, the local features on regions are extracted to capture visual appearance...
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries - signature forgeries that target a particular individual. In previous...
This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM. The proposed strategy reduces the false positive rate and increases the accuracy of detecting instances from novel classes. To this end, it uses two parallel hyperplanes to learn the normal region of the decision scores of the target class. OCSSVM extends one-class...
Nonverbal cues constitute a significant part of human communication. Traditionally the object of psychology, nonverbal communication studies now permeate fields such as social signal processing and human computer interaction. The ubiquity of digital recordings of human social interactions and of free sharing platforms offers many opportunities for the automated analysis of group interaction dynamics;...
Deep learning-based models have recently been widely successful at outperforming traditional approaches in several computer vision applications such as image classification, object recognition and action recognition. However, those models are not naturally designed to learn structural information that can be important to tasks such as human pose estimation and structured semantic interpretation of...
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images...
Attributes are defined as mid-level image characteristics shared among different categories. These characteristics are suitable in order to handle classification problems especially when training data are scarce. In this paper, we design discriminative real-valued attributes by learning nonlinear inductive maps. Our method is based on solving a constrained optimization problem that mixes three criteria;...
We present a novel algorithm for the semantic labeling of photographs shared via social media. Such imagery is diverse, exhibiting high intra-class variation that demands large training data volumes to learn representative classifiers. Unfortunately image annotation at scale is noisy resulting in errors in the training corpus that confound classifier accuracy. We show how evolutionary algorithms may...
We present an approach to automatically generating verbal commentaries for tennis games. We introduce a novel application that requires a combination of techniques from computer vision, natural language processing and machine learning. A video sequence is first analysed using state-of-the-art computer vision methods to track the ball, fit the detected edges to the court model, track the players, and...
Motivated by increasing popularity of depth visual sensors, such as the Kinect device, we investigate the utility of depth information in audio-visual speech activity detection. A two-subject scenario is assumed, allowing to also consider speech overlap. Two sensory setups are employed, where depth video captures either a frontal or profile view of the subjects, and is subsequently combined with the...
We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks,...
Facial expression recognition in complex environment is one of the difficult tasks of visual recognition in recent years. This paper introduces the visual saliency mechanism and we design automatic searching of the face region in the image. Using the narrow band C-V model to evolve curve, the proposed scheme can obtain the accurate face region. Meanwhile, the SVM will be trained by standard database...
Efficacy prediction is an inseparable part of TCM. We firstly analyze the correlation between indicators and efficacy, and max blood-drug concentration(Cmax) is chosen as the target to reflect the efficacy of drugs. Then we apply linear regression(LR), support vector regression(SVR) as well as artificial neural networks(ANNs) to predict the efficacy of Wuji pills. The results of the leave-one-out...
Feature representation and matching are two challenging problems for person e-identification problem. Designing a suitable feature representation method, and the according high efficiency matching scheme is meaningful. In this paper, a new person re-identification method was put forward. First, an improved BOF method was proposed, it use SURF algorithm to extract the preliminary feature and generate...
Target characterization of a biological network identifies characteristics that distinguish targets (nodes that can serve as molecular targets of drugs) from other nodes. In this demonstration, we present TENET (Target charactErization using NEtwork Topology), a software that facilitates topological features-based characterization of known targets in signaling networks modelling dynamic interactions...
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