What is the output of your kNN classifier? Did you obtain a label for each pixel? How is that label computed?

The output of a k-Nearest Neighbors (kNN) classifier is a class label. This label is assigned to the input data point based on the majority class of its 'k' nearest neighbors in the training dataset.

In the context of image classification, if we are classifying each pixel in the image, then yes, we would obtain a label for each pixel. Each pixel in the image is treated as a data point with its own set of features (such as color, intensity, etc.).

The label for each pixel is computed as follows:

1. The distance from the pixel (data point) to all other points in the training dataset is calculated. The distance measure could be Euclidean, Manhattan, Minkowski, etc.

2. The 'k' points in the training dataset that are closest to the pixel are identified.

3. The class labels of these 'k' points are then examined. The class that occurs the most frequently among these 'k' points is assigned as the label for the pixel.

This process is repeated for each pixel in the image, resulting in a label for each pixel.