Animal Classification System: A Block Based Approach
| Autor: | H.K. Chethan, Y. H. Sharath Kumar, N. Manohar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
PNN Computer science Computer Vision and Pattern Recognition (cs.CV) KNN ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition I.4.6 I.4.8 System a Probabilistic neural network Cut Computer vision Segmentation Color texture moments General Environmental Science business.industry Pattern recognition ComputingMethodologies_PATTERNRECOGNITION Animal classification Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences Artificial intelligence business Classifier (UML) Segmentation graph cut |
| Popis: | In this work, we propose a method for the classification of animal in images. Initially, a graph cut based method is used to perform segmentation in order to eliminate the background from the given image. The segmented animal images are partitioned in to number of blocks and then the color texture moments are extracted from different blocks. Probabilistic neural network and K-nearest neighbors are considered here for classification. To corroborate the efficacy of the proposed method, an experiment was conducted on our own data set of 25 classes of animals, which consisted of 4000 sample images. The experiment was conducted by picking images randomly from the database to study the effect of classification accuracy, and the results show that the K-nearest neighbors classifier achieves good performance. 8 pages, 2 figures, 3 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|