One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting ...

As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document ...

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic ...

In this work we propose a new keyframe extraction method based on SIFT local features. We extracted feature vectors from a carefully selected group of frames from a vídeo shot, analyzing those vectors to eliminate near ...

This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction ...

Techniques based on signal analysis for leak detection in water supply systems typically use long pressure and/or flow data series of variable length. This paper presents the feature extraction from pressure signals and ...

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature ...

Entropy (H) is the main subject of this article, concisely written to serve as a tutorial introducing two feature extraction (FE) methods for usage in digital signal processing (DSP) and pattern recognition (PR). The theory, ...

Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable ...