built and used to represent the image.

An inverted index file, used in the BoW framework, is an efficient image search tool in which the visual words are mapped to the database images. Each visual word serves as a table index and points to the indices of the database images in which the word occurs. Since not every image contains every word and also each word does not occur in every image, the retrieval through inverted-index file is fast.

Bag-of-Words for Image-based Localization {#sec:relwork}
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Bag-of-words model has been extensively used as the basis of the image search in appearance-based localization or SLAM algorithms [@fabmap; @fabmap2; @Achar; @Angeli; @bi-BoW]. Cummins and Newman [@fabmap; @fabmap2] propose a probabilistic framework over the bag-of-words representation of locations, for the appearance-based place recognition. Along with the visual vocabulary they also learn the Chow Liu tree to capture the co-occurrences of visual words. Similarly, Angeli *et al.* [@Angeli] develop a probabilistic approach for place recognition in SLAM. They build two visual vocabularies incrementally and use two BoW representations as an input of a Bayesian filtering framework to estimate the likelihood of loop closures.

Assuming each image has hundreds of SIFT features, mapping the features to 