Outdoor Landmark-view Recognition Based on Bipartite-graph Matching and Logistic Regression

This paper describes the extraction of visual landmarks from outdoor images for mobile robot applications. The concept of group of landmarks, called landmark-view, is introduced, aggregating the most relevant landmarks present in each scene. The relevance of the landmarks is determined by their relative visual saliency. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark descriptors, which are based on saliency and color distribution in chromaticity space. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against the single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. Of the 42 corresponding view pairs, 30 were recognized correctly, resulting in 71.4% of correct classification of similar views. Of the 948 non-corresponding view pairs, 768 were recognized correctly, resulting in 81.0% of correct classification in non-similar views. The overall percentage of correct view classification obtained was 80.6%, indicating the convenience of the approach.