bagofvisualwords

Bag of Visual Words (BoVW) is a foundational model in computer vision used to represent images for classification and retrieval tasks. Inspired by the bag-of-words representation in natural language processing, BoVW treats an image as a distribution over a fixed vocabulary of visual words.

The typical BoVW pipeline starts with detecting and describing local image features, such as SIFT, SURF, or ORB descriptors. A large collection of descriptors from a training set is clustered with an algorithm like k-means to form a visual vocabulary, where each cluster center is a visual word. For a given image, each descriptor is assigned to its nearest visual word, and the image is encoded as a histogram of word occurrences. Histograms are usually normalized to mitigate varying numbers of features per image.

Enhancements include soft assignment, where descriptors contribute to several nearby words, and spatial pyramids, which accumulate histograms at multiple spatial scales to preserve some layout information. The resulting BoVW feature vector is typically used with a linear classifier such as a support vector machine or logistic regression.

BoVW has been widely used for object recognition, scene categorization, and image retrieval. It is appreciated for its simplicity, clear interpretation, and scalability to large datasets. Limitations include the loss of precise spatial arrangement in the basic form, sensitivity to the choice of descriptors and vocabulary size, and the need for careful normalization. More modern approaches, including Fisher vectors, VLAD, and deep learning methods, have become popular alternatives but BoVW remains a foundational concept for understanding feature quantization in vision.