An Input image is blocked into several blocks and features are extracted from these blocks. Blocks are classified by K-NN classifier using training data with predefined labels, and the most frequently selected block label becomes the label of the image.
K-NN based scene classification system is not perfect in a practical situation because there are lots of ambiguous images which even a man cannot tell (indoor from outdoor), (city from landscape), (sunset from mountain&forest), (forest from mountain).
Thresholding approach is added to explicitly say that ambiguity exists, and this image has ambiguous label. This increases performance and completeness of previous K-NN based scene classification system.