Feature Based Underwater Fish Recognition Using SVM Classifier

An approach for underwater fish recognition based on wavelet transform is presented in this paper. This approach decomposes the input image into sub-bands by using the multi resolutional analysis known as Discrete Wavelet Transform (DWT). As each sub-band in the decomposed image contains useful information about the image, the mean values of every sub-band are assumed as features. This approach is tested on Underwater Photography - A Fish Database. The database contains 7953 pictures of 1458 different species. The database is considered for the classification based on Support Vector machine (SVM) classifier. The result shows that maximum recognition accuracy of 90.74% is achieved by the wavelet features.