![]() ![]() ![]() Thus, each patch in an image is mapped to a certain codeword through the clustering process and the image can be represented by the histogram of the codewords. The number of the clusters is the codebook size (analogous to the size of the word dictionary). Codewords are then defined as the centers of the learned clusters. One simple method is performing k-means clustering over all the vectors. A codeword can be considered as a representative of several similar patches. The final step for the BoW model is to convert vector-represented patches to "codewords" (analogous to words in text documents), which also produces a "codebook" (analogy to a word dictionary). 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). A good descriptor should have the ability to handle intensity, rotation, scale and affine variations to some extent. These vectors are called feature descriptors. Feature representation methods deal with how to represent the patches as numerical vectors. Feature representation Īfter feature detection, each image is abstracted by several local patches. Content based image indexing and retrieval (CBIR) appears to be the early adopter of this image representation technique. Ī definition of the BoW model can be the "histogram representation based on independent features". ![]() To achieve this, it usually includes following three steps: feature detection, feature description, and codebook generation. ![]() Similarly, "words" in images need to be defined too. To represent an image using the BoW model, an image can be treated as a document. Image representation based on the BoW model
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