For intelligent service robots, indoor scene classification is an important issue. To overcome the weak real-time performance of conventional algorithms, a new method based on Cloud computing is proposed for global image features in indoor scene classification. With MapReduce method, global PHOG feature of indoor scene image is extracted in parallel. And, feature eigenvector is used to train the decision classifier through SVM concurrently. Then, the indoor scene is validly classified by decision classifier. To verify the algorithm performance, we carried out an experiment with 350 typical indoor scene images from MIT LabelMe image library. Experimental results show that the proposed algorithm can attain better real-time performance. Generally, it is 1.4∼2.1 times faster than traditional classification methods which rely on single computation, while keeping stable classification correct rate as 70%.

Date Published: 11 July 2016
PDF: 8 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100110Z (11 July 2016); doi: 10.1117/12.2243308

Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)