Statistics of Feature Extraction by Topographic Independent Component Analysis from Natural Images
نویسندگان
چکیده
Our contribution highlights the statistical properties and biological interpretation of the basis vectors (filters) that result from applying topographic independent component analysis (ICA) to feature extraction from patches of natural images. The consistency of the feature sets obtained from various collections of natural image data sets applying topographical ICA (TICA) supports the opinion that the statistical properties of the environmental stimuli enforce a process according to some optimization criterion, which provides a good computational model for the response properties of sensory neurons. However, the basis vector set differs statistically meaningful from one image collection to the other, making the ICA decomposition of natural images unsuitable for a novel approach to image compression. Key-Words: Independent component analysis, Topography, Natural images, higher-order statistics
منابع مشابه
Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
متن کاملA review on EEG based brain computer interface systems feature extraction methods
The brain – computer interface (BCI) provides a communicational channel between human and machine. Most of these systems are based on brain activities. Brain Computer-Interfacing is a methodology that provides a way for communication with the outside environment using the brain thoughts. The success of this methodology depends on the selection of methods to process the brain signals in each pha...
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 کاملA comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation
Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amount...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2002