期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2014
卷号：5
期号：4
页码：55
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper, we propose a model for automatic classification of Animals using different classifiers NearestNeighbour, Probabilistic Neural Network and Symbolic. Animal images are segmented using maximalregion merging segmentation. The Gabor features are extracted from segmented animal images.Discriminative texture features are then selected using the different feature selection algorithm likeSequential Forward Selection, Sequential Floating Forward Selection, Sequential Backward Selection andSequential Floating Backward Selection. To corroborate the efficacy of the proposed method, anexperiment was conducted on our own data set of 25 classes of animals, containing 2500 samples. Thedata set has different animal species with similar appearance (small inter-class variations) across differentclasses and varying appearance (large intra-class variations) within a class. In addition, the images offlowers are of different poses, with cluttered background under different lighting and climatic conditions.Experiment results reveal that Symbolic classifier outperforms Nearest Neighbour and Probabilistic NeuralNetwork classifiers.
