Image Classifier

P.S.: Image Classification is a machine learning project idea from Clark University.

Machine learning image classification project has two parts: Training and Testing.

Training

In the training phase, the algorithm learns a function that maps an input to an output (or a label) using training data consisting of known input-output pairs. For the handwritten digit application, the training data comprise 60,000 grayscale images (inputs) and associated digits (labels).

Testing

In the testing phase, the algorithm uses the learned function to predict labels for unseen inputs. The algorithm makes some prediction errors (e.g., predicts 9 when the handwritten digit is 6). An important quality metric is the test error rate – the fraction of testing inputs that the algorithm misclassifies. It measures how well the learning algorithm generalizes from the training data to new data.

Here are some sample executions:

$ java-introcs ImageClassifier digits-training20.txt digits-testing10.txt 
digits/testing/1/46.png
digits/testing/7/36.png
digits/testing/7/80.png
digits/testing/1/40.png
digits/testing/1/39.png
digits/testing/7/79.png
digits/testing/9/20.png
digits/testing/9/58.png
test error rate = 0.8

$ java-introcs ImageClassifier digits-training60K.txt digits-testing10K.txt 
jar:file:digits.jar!/testing/5/9428.png
jar:file:digits.jar!/testing/6/4814.png
jar:file:digits.jar!/testing/5/4915.png
...
jar:file:digits.jar!/testing/5/7870.png
jar:file:digits.jar!/testing/4/1751.png
jar:file:digits.jar!/testing/5/6043.png
test error rate = 0.136

$ java-introcs ImageClassifier fashion-training60K.txt fashion-testing10K.txt 

$ java-introcs ImageClassifier Kuzushiji-training60K.txt Kuzushiji-testing10K.txt

$ java-introcs ImageClassifier fruit-training30K.txt fruit-testing6K.txt

$ java-introcs ImageClassifier animals-training60K.txt animals-testing12K.txt

$ java-introcs ImageClassifier music-training50K.txt music-testing10K.txt