Bird Species detection using Python

Trying to identify the bird species is a challenging task and often leads to ambiguous labels. Many times professional bird watchers fail to recognize the species of a bird from the image provided. Though many bird species are having the same basic set of parts like a beak, legs, feathers, etc, they can vary much in shape and appearance. The identification of birds species is a challenging task for both humans and computers. Factors like lighting, background or variation in positions (like swimming bird, flying bird) make a larger difference in recognizing the bird species for computers.

So in this python project, we are going to apply the power of machine learning with Python to identify the bird species from images.

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Project Description

The main idea of the project is to give bird images as input and print the name of it as output.

Algorithms used in the project:

• Naive Bayes: It is a collection of classification algorithms. It is based on Baye’s Theorem. It is a family of algorithms based on a common principle: every pair of features is independently classified of others.
• Support Vector Machines (SVM): Algorithm used to analyze data for classification and regression analysis. In addition to linear classification, SVM performs the non-linear classification also.
• Decision Trees: As the name suggests, they are the trees in which data is split according to certain parameters. The trees are defined by two entities viz. Decision nodes and leaves. Leaves are the final decisions or final outcomes. They are used to classify the data (Yes/No types tree).
• K-nearest Neighbours
• Linear Discriminant Analysis
• Random Forests
• One versus rest classifiers with Logistic Regression

Dataset used

Caltech-UCSD Birds-200-2011 (CUB-200-2011):

It contains 200 categories and 11788 images of birds.

Project Implementation

To complete the project do the following steps:

• Write an implementation of KNN and Naive Bayes in MATLAB.
• Use feature reducing using PCA and apply SVM, Logistic Regression and LDA on reduced features.
• Use selection techniques like L1 based method, remove features with low variance, univariate feature selection.
• Implement LDA, Logistic Regression and SVM.
• Include certainty values of features into the model.

Results

After training and testing the algorithm on the complete dataset we are able to identify the bird species with a testing accuracy of 51.6%.

Software requirements: Pycharm and Python3.

Programming  Languages: Python, Scikit Learn library.

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Bird Species detection using Python