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.

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

“…Color based CENTRIST is applied in [16] by combining the values in HSV color space. Finally, in [17], histograms of oriented uniform LBPs are extracted from images to categorize places indoors and outdoors. Finally, visual cues have been used in combination with 2D laser to improve the final classification in [18].…”

DEDUCE: Diverse scEne Detection methods in Unseen Challenging Environments

“…Since all the scenes are not present in some of the floors, some of the cells are kept empty. We compare the average accuracy obtained per scene over the 6 houses, and compare the result with that reported in [38]. Clearly, both our algorithms outperform the reported score by a long margin, with the only exception of the bathroom class.…”

“…We first consider the methods described in [5], which use SIFT and CENTRIST features with a Nearest Neighbor Classifier, and also exploit temporal information between images by coupling them with Bayesian Filtering (BF). Next, we look at the approach of [38] where Histogram of Oriented Uniform Patterns (HOUP) is used as input to the same classifier. [39] proposed the method of using object templates for visual place categorization, and reported results for Global configurations approach with Bayesian Filtering (G+BF), and that combined with the object templates (G+O(SIFT)+BF).…”

Indoor Place Categorization Using Co-occurrences of LBPs in Gray and Depth Images from RGB-D Sensors

“…The CENTRIST descriptor is extended to HSV color space in [12]. Finally, the approach in [9] uses uniform LPBs to categorize indoor and outdoor places.…”

“…The problem of place categorization has been addressed by several researchers using different type of sensors like for example laser scans [3], [4], [5], or vision cameras [6], [7], [8], [9]. Recently, RGB-D sensors are getting popular in robotics due to its low cost and the multi-modal nature of the provided information: RGB and depth data.…”