No abstract

Now a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios appear in different sizes, shapes, colors, and angles from human perspective. Besides, the images present strong variations to identify the bird species more than audio classification. Also, human ability to recognize the birds through the images is more understandable. So, this method uses the Caltech-UCSD Birds 200 [CUB-200-2011] dataset for training as well as testing purpose. By using deep convolutional neural network (DCNN) algorithm an image converted into grey scale format to generate autograph by using tensor flow, where the multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet, it can predicate the required bird species by using highest score. Experimental analysis on dataset (i.e., Caltech-UCSD Birds 200 [CUB-200-2011]) shows that algorithm achieves anaccuracy of bird identification between 80% and 90%. The experimental study is done with the Ubuntu 16.04 operating system using a Tensor flow library. KEYWORDS: Caltech-UCSD; grey scale pixels; TensorFlow.