Python codebook1/30/2024 The outline of the K-Means clustering is shown below – This step normally accomplished via the k-means clustering algorithm. This codeword also produces a codebook is similar to a word dictionary Codewords are nothing but vector representation of similar patches. The vectors generated in the feature extraction step above are now converted into the codewords which is similar to words in text documents. Feature Extraction (reference- ) Codewords and Codebook Construction After this step, each image is a collection of vectors of the same dimension (128 for SIFT), where the order of different vectors is of no importance. SIFT converts each patch to 128-dimensional vector. One of the most famous descriptors is Scale-invariant feature transform (SIFT) and another one is ORB. These vectors are called feature descriptors.Ī good descriptor should have the ability to handle the intensity, rotation, scale and affine variations to some extent. The first step to build a bag of visual words is to perform feature extraction by extracting descriptors from each image in our dataset.įeature representation methods deal with how to represent the patches as numerical vectors. Let us go through each of the steps in detail. Classification – Classification of images based on vocabulary generated using SVM.
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