A Simple Bayesian Framework for Content-Based Image Retrieval

• Published in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) Vol. 2; pp. 2110 - 2117
• Main Authors: Heller, K.A., Ghahramani, Z.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2006
• Subjects: Bayesian methods; Computer vision; Content based retrieval; Distributed computing; Educational institutions; Gabor filters; Histograms; Image databases; Image retrieval; Sparse matrices
• ISBN: 9780769525976; 0769525970
• ISSN: 1063-6919; 1063-6919
• DOI: 10.1109/CVPR.2006.41

We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a userspecified text query (e.g. "penguins") the system first extracts a set of images, from a labelled corpus, corresponding to that query. The distribution over features of these images is used to compute a Bayesian score for each image in a large unlabelled corpus. Unlabelled images are then ranked using this score and the top images are returned. Although the Bayesian score is based on computing marginal likelihoods, which integrate over model parameters, in the case of sparse binary data the score reduces to a single matrix-vector multiplication and is therefore extremely efficient to compute. We show that our method works surprisingly well despite its simplicity and the fact that no relevance feedback is used. We compare different choices of features, and evaluate our results using human subjects.