9 research outputs found
Gait-based Gender Classification Considering Resampling and Feature Selection
Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking public databases and renowned gait representations lead to these two problems, but they have not been jointly studied in depth. This paper is a preliminary study that pursues to investigate the benefits of using several techniques to tackle the aforementioned problems either singly or in combination, and also to evaluate the order of application that leads to the best classification performance. Experimental results show the importance of jointly managing both problems for gait-based gender classification. In particular, it seems that the best strategy consists of applying resampling followed by feature selection
Gait recognition from corrupted silhouettes: a robust statistical approach
This paper introduces a method based on robust statistics to build reliable gait signatures from averaging silhouette descriptions, mainly when gait sequences are affected by severe and persistent defects. The term robust refers to the ability of reducing the impact of silhouette defects (outliers) on the average gait pattern, while taking advantage of clean silhouette regions. An extensive experimental framework was defined based on injecting three types of realistic defects (salt and pepper noise, static occlusion, and dynamic occlusion) to clean gait sequences, both separately in an easy setting and jointly in a hard setting. The robust approach was compared against two other operation modes: (1) simple mean (weak baseline) and (2) defect exclusion (strong benchmark). Three gait representation methods based on silhouette averaging were used: Gait Energy Image (GEI), Gradient Histogram Energy Image (GHEI), and the joint use of GEI and HOG descriptors. Quality of gait signatures was assessed by their discriminant power in a large number of gait recognition tasks. Nonparametric statistical tests were applied on recognition results, searching for significant differences between operation modes.This work has been supported by the grants P1-1B2012-22 and PREDOC/2012/05 from Universitat Jaume I, PROMETEOII/2014/062 from Generalitat Valenciana, and TIN2013-46522-P from Spanish Ministry of Economy and Competitiveness
Surrounding neighborhood-based SMOTE for learning from imbalanced data sets
Many traditional approaches to pattern classiïŹ-
cation assume that the problem classes share similar prior
probabilities. However, in many real-life applications, this
assumption is grossly violated. Often, the ratios of prior probabilities between classes are extremely skewed. This situation
is known as the class imbalance problem. One of the strategies to tackle this problem consists of balancing the classes
by resampling the original data set. The SMOTE algorithm
is probably the most popular technique to increase the size of
the minority class by generating synthetic instances. From the
idea of the original SMOTE, we here propose the use of three
approaches to surrounding neighborhood with the aim of
generating artiïŹcial minority instances, but taking into
account both the proximity and the spatial distribution of the
examples. Experiments over a large collection of databases
and using three different classiïŹers demonstrate that the new
surrounding neighborhood-based SMOTE procedures
signiïŹcantly outperform other existing over-sampling algorithms
A GENDER RECOGNITION EXPERIMENT ON THE CASIA GAIT DATABASE DEALING WITH ITS IMBALANCED NATURE
Abstract: The CASIA Gait Database is one of the most used benchmarks for gait analysis among the few non-smallsize datasets available. It is composed of gait sequences of 124 subjects, which are unequally distributed, comprising 31 women and 93 men. This imbalanced situation could correspond to some real contexts where men are in the majority, for example, a sports stadium or a factory. Learning from imbalanced scenarios usually requires suitable methodologies and performance metrics capable of managing and explaining biased results. Nevertheless, most of the reported experiments using the CASIA Gait Database in gender recognition tasks limit their analysis to global results obtained from reduced subsets, thus avoiding having to deal with the original setting. This paper uses a methodology to gain an insight into the discriminative capacity of the whole CASIA Gait Database for gender recognition under its imbalanced condition. The classification results are expected to be more reliable than those reported in previous papers
Team activity recognition in Association Football using a Bag-of-Words-based method
In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based on the Bag-of-Words (BoW) technique is used to characterize short Football video clips that are used to explain the teamâs performance and to train advanced classifiers in automatic recognition of team activities. In addition to the neural network-based classifier, three more classifier families are tested: the k-Nearest Neighbor, the Support Vector Machine and the Random Forest. The results obtained show that the proposed methodology is able to explain the most common movements of a team and to perform the team activity recognition task with high accuracy when classifying three Football actions: Ball Possession, Quick Attack and Set Piece. Random Forest is the classifier obtaining the best classification results
ATM-based analysis and recognition of handball team activities
In this paper, a new methodology based on the Author Topic Model (ATM) method is presented to perform team activity recognition and analysis in handball videos. Instead of using playersŚł trajectories we just rely on low level features related to local motion, the evolution of which is then modeled over time by the ATM. The proposed methodology is applied to the task of recognizing four kinds of team activities in handball videos from the CVBASEŚł06 dataset and to analyze which are the most important elements of the activities. Our method is compared with two other ways of characterizing videos based on Bag-of-Words (BoW) and Latent Dirichlet Allocation (LDA) techniques. Our proposal obtains competitive results in terms of accuracy, computing time and interpretation of the results.The authors acknowledge the FundaciĂł Caixa-CastellĂł Bancaixa under project P1-1A2010-11