In this paper, we address the challenging problem of single-view cross-season place recognition. A new approach is proposed for compact discriminative scene descriptor that helps in coping with changes in appearance in the environment. We focus on a simple effective strategy that uses objects whose appearance remain the same across seasons as valid landmarks. Unlike popular bag-of-words (BoW) scene descriptors that rely on a library of vector quantized visual features, our descriptor is based on a library of raw image data (e.g., visual experience shared by colleague robots, publicly available photo collections from Google StreetView), and directly mines it to identify landmarks (i.e., image patches) that effectively explain an input query/database image. The discovered landmarks are then compactly described by their pose and shape (i.e., library image ID, and bounding boxes) and used as a compact discriminative scene descriptor for the input image. We collected a dataset of single-view images across seasons with annotated ground truth, and evaluated the effectiveness of our scene description framework by comparing its performance to that of previous BoW approaches, and by applying an advanced Naive Bayes Nearest neighbor (NBNN) image-to-class distance measure.

We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available visual experience, images shared by other colleague robots, and publicly available image data on the web) and directly mine it to find visual phrases (VPs) that discriminatively and compactly explain an input query / database image. Our mining approach is motivated by recent success in the field of common pattern discovery-specifically mining of common visual patterns among scenes-and requires only a single library of raw images that can be acquired at different time or day. Experimental results show that even though our scene descriptor is significantly more compact than conventional descriptors it has a relatively higher recognition performance.