A Identifying Points of Interest using Heterogeneous Features

Deducing trip related information from web-scale datasets has received large amounts of attention recently. Identifying points of interest (POIs) in geo-tagged photos is one of these problems. The problem can be viewed as a standard clustering problem of partitioning two dimensional objects. In this work, we study spectral clustering which is the first attempt for the POIs identification. However, there is no unified approach to assign the subjective clustering parameters; especially these parameters are immensely varying in different metropolitans and locations. To address this issue, we are intent to study a self-tuning technique which can properly determine the parameters for the clustering needed. Besides geographical information, web photos inherently store other rich information. Such heterogenous information can be used to enhance the identification accuracy. Thereby, we study a novel refinement framework which is based on the tightness and cohesion degree of the additional information. At last, we thoroughly demonstrate our findings by web scale datasets collected from Flickr.