Description

Image Category Classification using Bag of features

In computer vision and image analysis, the bag-of-words model (BoW model, also known as bag-of-features) can be applied to achieve image classification, by treating image features as words. In textual document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. The selected keywords forming the bag of words represents the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features. Image category classification (categorization) is the process of assigning a category label to an image under test. Categories may contain images representing just about anything, for example, dogs, cats, trains, boats. In this project, we use a bag of features approach for image category classification.

Demo video

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