In this paper we present an approach to music genre classification which converts an audio signal into spectrograms and extracts texture features from these time-frequency images which are then used for modeling music genres in a classification system. The texture features are based on Local Binary Pattern, a structural texture operator that has been successful in recent image classification research. Experiments are performed with two well-known datasets: the Latin Music Database (LMD), and the ISMIR 2004 dataset. The proposed approach takes into account some different zoning mechanisms to perform local feature extraction. Results obtained with and without local feature extraction are compared. We compare the performance of texture features with that of commonly used audio content based features (i.e. from the MARSYAS framework), and show that texture features always outperforms the audio content based features. We also compare our results with results from the literature. On the LMD, the performance of our approach reaches about 82.33%, above the best result obtained in the MIREX 2010 competition on that dataset. On the ISMIR 2004 database, the best result obtained is about 80.65%, i.e. below the best result on that dataset found in the literature.

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Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texturebased features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior.

In this work we focus on investigating the use of multiple feature vectors for forest species recognition. As consequence, we propose a framework to deal with the extraction of multiple feature vectors based on two approaches: image segmentation and multiple feature sets. Experiments conducted on a 112 species database containing microscopic images of wood demonstrate that with the proposed framework we can increase the recognition rates of the system from about 55.7% (with a single feature vector) to about 93.2%.