Human Action Recognition Using Independent Component Analysis
نویسندگان
چکیده
Principal Component Analysis (PCA) is often used for reducing the dimensionality of input feature space. However, the eigenspace based on PCA is not always the best feature space for pattern recognition. In this paper, we use the feature space based on Independent Component Analysis (ICA) and show that the ICA representation is more effective than the PCA representation for human action recognition. The experimental results on the human action database show that the ICA approach produces more accurate recognition than the PCA approach.
منابع مشابه
Recognizing facial action units using independent component analysis and support vector machine
Facial expression provides a crucial behavioral measure for studies of human emotion, cognitive processes, and social interaction. In this paper, we focus on recognizing facial action units (AUs), which represent the subtle change of facial expressions. We adopt ICA (independent component analysis) as the feature extraction and representation method and SVM (support vector machine) as the patte...
متن کاملEfficiency Measurement of Clinical Units Using Integrated Independent Component Analysis-DEA Model under Fuzzy Conditions
Background and Objectives: Evaluating the performance of clinical units is critical for effective management of health settings. Certain assessment of clinical variables for performance analysis is not always possible, calling for use of uncertainty theory. This study aimed to develop and evaluate an integrated independent component analysis-fuzzy-data envelopment analysis approach to accurate ...
متن کاملHuman Action Recognition Using Tensor Principal Component Analysis
Human action can be naturally represented as multidimensional arrays known as tensors. In this paper, a simple and efficient algorithm based on tensor subspace learning is proposed for human action recognition. An action is represented as a 3th-order tensor first, then tensor principal component analysis is used to reduce dimensionality and extract the most useful features for action recognitio...
متن کاملSupervised Feature Extraction of Face Images for Improvement of Recognition Accuracy
Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...
متن کاملSpeech feature extraction using independent component analysis
In this paper, we proposed new speech features using independent component analysis to human speeches. When independent component analysis is applied to speech signals for efficient encoding the adapted basis functions resemble Gabor-like features. Trained basis functions have some redundancies, so we select some of the basis functions by reordering method. The basis functions are almost ordere...
متن کامل