Search

This paper presents a project called KnowIng camera prototype SyStem (KISS) for real-time places-of-interest (POI) recognition and annotation for smartphone photos, with the availability of online geotagged images for POIs as our knowledge base. We propose a “Spatial+Visual” (S+V) framework which consists of a probabilistic field-of-view (pFOV) model in the spatial phase and sparse coding similarity metric in the visual phase to recognize phone-captured POIs. Moreover, we put forward an offline Collaborative Salient Area (COSTAR) mining algorithm to detect common visual features (called Costars) among the noisy photos geotagged on each POI, thus to clean the geotagged image database. The mining result can be utilized to annotate the region-of-interest on the query image during the online query processing. Besides, this mining procedure also improves the efficiency and accuracy of the S+V framework. Furthermore, we extend the pFOV model into a Bayesian FOV( $\beta$ FOV) model which improves the spatial recognition accuracy by more than 30 percent and also further alleviates visual computation. From a bayesian point of view, the likelihood of a certain POI being captured by phones is a prior probability in pFOV model which is represented as a posterior probability in $\beta$ FOV model.Our experiments in the real-world and Oxford 5K datasets show promising recognition results. In order to provide a fine-grained annotation ground truth, we labeled a new dataset based on Oxford 5K and make it public available on the web. Our COSTAR mining techniqueoutperforms state-of-the-art approach on both dataset. [ABSTRACT FROM PUBLISHER]

The accurate knowledge of tire–road friction coefficient (TRFC) contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles. The performance of the existing estimation methods would decline when a vehicle performs complex driving maneuvers. In addition, the mass parameter mismatch also deteriorates the estimation accuracy of TRFC. To address these problems, in this article, an integrated scheme for TRFC estimation is proposed by combining a strong tracking unscented Kalman filter and an interactive multiple model unscented Kalman filter. Real-time experiments are implemented on a mass-produced vehicle to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed approach has better estimation accuracy than the existing ones under various driving scenarios. [ABSTRACT FROM AUTHOR]

Aiming at improving the performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called deep co-space (DCS). Unlike many conventional semi-supervised learning methods usually performed within a fixed feature space, our DCS gradually propagates information from labeled samples to unlabeled ones along with deep feature learning. We regard deep feature learning as a series of steps pursuing feature transformation, i.e., projecting the samples from a previous space into a new one, which tends to select the reliable unlabeled samples with respect to this setting. Specifically, for each unlabeled image instance, we measure its reliability by calculating the category variations of feature transformation from two different neighborhood variation perspectives and merged them into a unified sample mining criterion deriving from Hellinger distance. Then, those samples keeping stable correlation to their neighboring samples (i.e., having small category variation in distribution) across the successive feature space transformation are automatically received labels and incorporated into the model for incrementally training in terms of classification. Our extensive experiments on standard image classification benchmarks (e.g., Caltech-256 and SUN-397) demonstrate that the proposed framework is capable of effectively mining from large-scale unlabeled images, which boosts image classification performance and achieves promising results compared with other semi-supervised learning methods. [ABSTRACT FROM AUTHOR]