Incremental visual objects clustering with the growing vocabulary tree
With the bag-of-visual-words image representation, we can use the text analysis methods, such as pLSA and LDA, to solve the visual objects clustering and classification problems. However the previous works only used a fixed visual vocabulary, which is formed by vector quantizing SIFT like region descriptors, and so the learned visual topic models are also only based on the fixed vocabulary. This paper presents a novel approach to cluster visual objects in an incremental manner. Given a new batch of images, we firstly expand the visual vocabulary to include the new visual words, and then adjust the objects clustering model to absorb these new words, and finally give the clustering result. We achieve our goal by adapting to the visual domain of the incremental pLSA model previously used for text analysis. Experimental results demonstrate the feasibility and stability of the growing vocabulary tree and the clustering performance using the images from seven categories in a dynamic environment.
incremental plsa visual clustering bag-of-words