Histogram of Oriented Uniform Patterns for robust place recognition and categorization

Abstract

This paper presents a novel context-based scene recognition method that enables mobile robots to recognize previously observed topological places in known environments or categorize previously unseen places in new environments. We achieve this by introducing the Histogram of Oriented Uniform Patterns (HOUP), which provides strong discriminative power for place recognition, while offering a significant level of generalization for place categorization. HOUP descriptors are used for image representation within a subdivision framework, where the size and location of sub-regions are determined using an informative feature selection method based on kernel alignment. Further improvement is achieved by developing a similarity measure that accounts for perceptual aliasing to eliminate the effect of indistinctive but visually similar regions that are frequently present in outdoor and indoor scenes. An extensive set of experiments reveals the excellent performance of our method on challenging categorization and recognition tasks. Specifically, our proposed method outperforms the current state of the art on two place categorization datasets with 15 and 5 place categories, and two topological place recognition datasets, with 5 and 27 places.