MATLAB Implementation of Bag of Words Model

This MATLAB implementation demonstrates the Bag of Words (BoW) model, a fundamental algorithm widely used in image classification and text processing applications. The core concept involves converting input data (such as images or text) into fixed-length feature vectors through a systematic pipeline. Feature Extraction In MATLAB implementations, feature detection typically employs algorithms like SURF (Speeded-Up Robust Features) or SIFT (Scale-Invariant Feature Transform). The workflow begins with using functions like `detectSURFFeatures` to identify keypoints, followed by `extractFeatures` to generate descriptors. These functions return numerical matrices where each row represents a feature descriptor, forming the basis for vocabulary construction. Visual Vocabulary Construction The extracted feature descriptors undergo clustering using K-means algorithm to form visual words. MATLAB's `kmeans` function processes the descriptor matrix with syntax: `[idx, C] = kmeans(descriptors, k)`, where k determines the vocabulary size, idx contains cluster indices, and C stores cluster centroids representing the codebook. Feature Encoding Each image's descriptors are mapped to the visual vocabulary through histogram generation. This involves calculating Euclidean distances between descriptors and cluster centers using `pdist2`, followed by frequency counting of the nearest visual words. The resulting histogram vector (normalized using `histcounts` or manual normalization) serves as the final BoW representation. Classification Application The encoded features can be utilized with classifiers like SVM (Support Vector Machine) for training and prediction. MATLAB provides `fitcecoc` for multi-class classification using error-correcting output codes, or `fitcsvm` for binary classification. The classification workflow typically involves: 1. Training with `classifier = fitcecoc(trainingFeatures, trainingLabels)` 2. Predicting with `predictedLabels = predict(classifier, testFeatures)` While the Bag of Words model maintains simplicity, it demonstrates strong performance in small-scale image classification tasks. MATLAB's efficient matrix operations and comprehensive toolboxes make it an ideal environment for rapid BoW implementation, particularly through vectorized operations that optimize computation speed.