Bag-of-Visual Words based Improved Image Retrieval Algorithm for Vision Indoor Positioning

Aiming at the problem of existing bag-of-visual words based image retrieval algorithm, such as poor stability and low retrieval accuracy, a bag-of-visual words based improved image retrieval algorithm (IBVW) is proposed, which extracts features from the images in the database. The approximate K-means algorithm is adopted to cluster the image features into visual words and store them in the database. And then, the feature extraction and the inverted index in the online stage are implemented on the query image to find the images with high similarity in the database. At last, the best matching image is acquired through the similarity calculation, voting scheme and homography based matching algorithm. Simulation results and performance analysis show that the accuracy of our retrieval algorithm is improved by about 10% compared with existing methods for vision indoor positioning.