Texture analysis has many areas of potential application in industry. The problem of determining composition of grain mixtures by texture analysis was recently studied by Kjell. He got promising results when using all nine Laws’ 3x3 features simultaneously and an ordinary feature vector classifier. In this paper the performance of texture classification based on feature distributions in this pr...

Feature construction is essential for solving many complex learning problems. Unfortunately, the construction of features usually implies searching a very large space of possibilities and is often computationally demanding. In this work, we propose a case based approach to feature construction. Learning tasks are stored together with a corresponding set of constructed features in a case base an...

Motor imagery classification in electroencephalography (EEG)-based brain–computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the stateof-the-art approaches. To ...

The work presented in this paper focuses on recognition of isolated handwritten numerals in Devanagari and Gurumukhi script. The proposed work uses four feature extraction methods like Zoning density, Projection histograms, Distance profiles and Background Directional Distribution(BDD). On the basis of these four types of features we have formed 10 feature vectors using different combinations o...

Musical genre classification task falls into two major stages: feature extraction and classification. The latter implies a choice of a variety of machine leaning methods, as support vector machines, neural networks, etc. However, the former stage provides much more creativity in development of musical genre classification system and it plays crucial part in performance of the system as a whole....

In the context of signal classification, this paper assembles and compares criteria to easily judge the discrimination quality of a set of feature vectors. The quality measures are based on the assumption that a Support Vector Machine is used for the final classification. Thus, the ultimate criterion is a large margin separating the two classes. We apply the criteria to control the feature extr...

This paper presents a biomedical image retrieval approach by detecting affine covariant regions and representing them with an invariant fuzzy feature space. The covariant regions simply refers to a set of pixels or interest points which change covariantly with a class of transformations, such as affinity. A vector descriptor based on Scale-Invariant Feature Transform (SIFT) is then associated w...

In this paper, we present an approach that unifies sub-space feature extraction and support vector classification for face recognition. Linear discriminant, independent component and principal component analyses are used for dimensionality reduction prior to introducing feature vectors to a support vector machine. The performance of the developed methods in reducing classification error and pro...

We propose a classification technique for face expression recognition using AdaBoost that learns by selecting the relevant global and local appearance features with the most discriminating information. Selectivity reduces the dimensionality of the feature space that in turn results in significant speed up during online classification. We compare our method with another leading margin-based clas...