Common Crucial Feature for Crowdsourcing Based Mobile Visual Location Recognition
Hao Wang, Dong Yang Zhao, Huadóng Ma, Yumeng Liang · 2018
Crowdsourcing provides a novel and effective way of constructing a location image database for mobile visual location recognition. Compared with traditional location image databases, a crowdsourced database has richer information for location images, with various angles, times, distances and weathers, providing great potential for high recognition accuracy. However, it is inevitable to have various disturbances on these location images, hindering the potential. To address this challenge, we first propose a Common Crucial Feature (CCF) detection algorithm to exclude unimportant visual features from crucial features. To achieve a good balance between the efficiency and accuracy, we further propose a CCF based Visual Hash Bits (VHB) scheme to encode CCF features into hash bits to vote for most matching images. Extensive experiments are conducted on a crowdsourced dataset with 9,064 location images, demonstrating that our scheme outperforms other state-of-the-art schemes.