This paper presents a thorough study into the influence of the image resolution on automatic face gender classification. The images involved range from extremely low resolutions (2 × 1 pixels) to full face sizes (329 × 264 pixels). A comprehensive comparison of the performances achieved by two classifiers using ten different image sizes is provided by means of two performance measures Correct Classification Rate (CCR) and Geometric Mean (GMean). Single- and cross-database experiments are designed over three well-known face datasets. A detailed statistical analysis of the results revealed that a face as small as 3 × 2 pixels carries some useful information for distinguishing between genders. However, in situations where higher resolution face images are available, moderately sized faces from 22 × 18 to 90 × 72 pixels are optimal for this task. Furthermore, the performance of the classifiers was robust to the changes in the image resolution (using medium to full sizes). Only when the image resolution was reduced to 8 × 6 pixels or smaller, the classification results were significantly affected.

An unsupervised method for selecting training data is suggested here. The method is tested by applying it to hyperspectral land-use classification. The data set is reduced using an unsupervised band selection method and then clustered with a nonparametric cluster technique. The cluster technique provides centers of the clusters, and those are the samples selected to compose the training set. Both the band selection and the clustering are unsupervised techniques. Afterward, an expert labels those samples, and the rest of unlabeled data can be classified. The inclusion of the selection step, although unsupervised, allows to select automatically the most suitable pixels to build the classifier. This reduces the expert effort because less pixels need to be labeled. However, the classification results are significantly improved in comparison with the results obtained by a random selection of training samples, in particular for very small training sets.

This paper presents a statistical study of local vs. global approaches for classifying gender from neutral and expressive faces. A cross-dataset evaluation is provided by using different training and test face databases, as well as several well-known classifiers (1-NN, PCA+LDA and SVM) and widely used features for facial description. Three statistical tests have proved that local approaches are more suitable than global ones for solving gender classification problems over expressive faces when training with non-expressive faces. However, if a large set of expressive faces is available for training, global solutions outperform local ones.

This letter presents a spectral-spatial pixel characterization method for hyperspectral images. The characterization is based on textural features obtained using Gabor filters over a selected set of spectral bands. This scheme aims at improving land-use classification results, decreasing significantly the number of spectral bands needed in order to reduce the dimensionality of the task owing to an adequate description of the spatial characteristics of the image. This allows requiring less data and avoiding the curse of dimensionality. Very promising results are obtained which are similar to or better than previous classification results provided by other spectral-spatial methods but here also reducing the complexity using a reduced number of spectral bands.

Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. This aim is pursued using information measures, such as distances based on mutual information or Kullback–Leibler divergence, in order to reduce data redundancy and nonuseful information among image bands. Experimental results include a comparison among some relevant and recent methods for hyperspectral band selection using no labeled information, showing their performance with regard to pixel image classification tasks. The technique that is presented has a stable behavior for different image data sets and a noticeable accuracy, mainly when selecting small sets of bands.

Most of texture classification techniques are evaluated using large rectangular samples of each texture. This, however, is unrealistic, especially as samples of a certain texture may be used as cues in searching image databases or identifying objects in a scene. In this paper, four different texture classification methods (wavelets, co-occurrence matrices, sum and difference histograms, and 1D Boolean models) are systematically compared and evaluated with respect to their performance in identifying textures from small and irregular samples.

The one-dimensional (1-D) Boolean model is used to calculate features for the description of binary textures. Each two-dimensional (2-D) texture is converted into several 1-D strings by scanning it according to raster vertical, horizontal or Hilbert sequences. Several different probability distributions for the segment lengths created this way are used to model their distribution. Therefore, each texture is described by a set of Boolean models. Classification is performed by calculating the overlapping probability between corresponding models. The method is evaluated with the help of 32 different binary textures, and the pros and cons of the approach are discussed.

We generalise the use of the 1D Boolean model for the analysis of grey textures. Each grey image is first histogram-equalised with 8 grey levels. The pixels that constitute each grey level are used to form a binary image. Each of these binary images is separately analysed with the help of the 1D Boolean model and features are extracted from it. The final texture recognition is performed on the basis of these features. 100% recognition rate is achieved for an image database of Brodatz textures that consists of 48 model textures coming from 16 different classes and 16 test textures, one from each class.