Clustering Images Using the Latent Dirichlet Allocation Model

Clustering, in simple words, is grouping similar data items together. In the text domain, clustering is largely popular and fairly successful. In this work, we try and apply clustering methods that are used in the text domain, to the image domain. Two major challenges in this approach are image representation and vocabulary definition. We apply the bag-of-words model to images using image segments as words. We use the Latent Dirichlet Allocation (LDA) to model the relationships between “words” of an image, and between images. This provides us with a highly compressed yet succinct representation of an image, which can be further used for various applications like image clustering, image retrieval and image relevance ranking. In this work, we have used the relationships obtained from LDA to cluster the images with 78% success.