Artificial Intelligence and Robotics | Computer Engineering | Graphics and Human Computer Interfaces | Robotics
Abstract—Landmarks can be used as reference to enable people or robots to localize themselves or to navigate in their environment. Automatic definition and extraction of appropriate landmarks from the environment has proven to be a challenging task when pre-defined landmarks are not present. We propose a novel computational model of automatic landmark detection from a single image without any pre-defined landmark database. The hypothesis is that if an object looks abnormal due to its atypical scene context (what we call surprise saliency), it then may be considered as a good landmark because it is unique and easy to spot by different viewers (or the same viewer at different times). We leverage stateof- the-art algorithms based on convolutional neural networks to recognize scenes and objects. For each detected object, a surprise saliency score, a fusion of scene and object information, is calculated to determine if it is a good landmark. In order to evaluate the performance of the proposed model, we collected a landmark image dataset which consists of landmark images, as we have defined them with surprise saliency above, and non-landmark images. The experimental results show that our model achieves good performance in automatic landmark detection and automatic landmark image classification.
2016 IEEE International Conference on Multisensor Fusion and Integration, Baden-Baden, Germany during 19 - 21 September 2016.
Tang, F., Lyons, D., Leeds, D. "Landmark Detection with Surprise Saliency Using Convolutional Neural Networks" 2016 IEEE International Conference on Multisensor Fusion and Integration, Baden-Baden, Germany during 19 - 21 September 2016.
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