Rapid image retrieval for mobile location recognition

Recognizing the location and orientation of a mobile device from captured images is a promising application of image retrieval algorithms. Matching the query images to an existing georeferenced database like Google Street View enables mobile search for location related media, products, and services. Due to the rapidly changing field of view of the mobile device caused by constantly changing user attention, very low retrieval times are essential. These can be significantly reduced by performing the feature quantization on the handheld and transferring compressed Bag-of-Feature vectors to the server. To cope with the limited processing capabilities of handhelds, the quantization of high dimensional feature descriptors has to be performed at very low complexity. To this end, we introduce in this paper the novel Multiple Hypothesis Vocabulary Tree (MHVT) as a step towards real-time mobile location recognition. The MHVT increases the probability of assigning matching feature descriptors ...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Georg Schroth, Anas Al-Nuaimi, Robert Huitl, Florian Schweiger, Eckehard G. Steinbach
• Keywords: Dimensional Feature Descriptors, Feature Descriptors, ICASSP 2011, Mobile Device, Signal Processing