Since Wireless Capsule Endoscopy (WCE) is a novel technology for recording the videos of the digestive tract of a patient

the problem of segmenting the WCE videos of the digestive tract into sub-images corresponding to the mouth

stomach

small intestine and large intestine regions is not well addressed in the literature.A few papers addressing this problem use a supervised learning approach that presumes availability of a large database of correctly labeled training samples.Considering the difficulties in procuring sizable WCE training data sets needed for achieving high classification accuracy

we introduce an unsupervised learning approach that employs Scale invariant feature transform (SIFT) with color information for extraction of local features and uses probabilistic latent semantic analysis (pLSA) model for data semantic analysis.Our results indicate that this method compares well in classification accuracy with the state-of-the-art supervised classification approach to WCE image classification.