Large-Scale Scene Recognition Based on Extreme Learning Machines

For intelligent robots, scene recognition aims to find a semantic explanation of a scene, i.e., it helps the robots to know where they are. It can be widely applied into various robotic tasks, e.g, topological localization, simultaneous localization and mapping and autonomous navigation. Many of existing methods for scene recognition focused on how to build scene features, such as holistic representations and bags of visual words. However, less attention is put on the classification. Due to the huge number of scene classes in the real world, the variances within each class and the shared features between classes, the classification becomes a challenging issue for scene recognition. This paper proposes an ensemble method for large-scale scene recognition. This proposed method builds a three-level hierarchy for recognizing 397 classes of scenes in the real world. At each level, an ensemble-based classifier is built by using 13 types of features. Extreme learning machine is employed as the basic classifier in each ensemble-based classifier. Experimental results have shown that this proposed method outperforms other state-of-the-art methods in terms of recognition accuracy.