Dissertations and Theses
Date of Award
k-means; clustering; geometric; verification; Indian-buffet; stochastic; suspicious; hierarchical tree
This paper describes an approach to identify individuals with suspicious objects in a crowd. It is based on a well-known image retrieval problem as applied to mobile visual search. In many cases, the process of building a hierarchical tree uses k-means clustering followed by geometric verification. However, the number of clusters is not known in advance, and sometimes it is randomly generated. This may lead to a congested clustering which can cause problems in grouping large real-time data. To overcome this problem we have applied the Indian Buffet stochastic process approach in this paper to the clustering problem. We present examples illustrating our method
Mukherjee, Satabdi, "AN APPROACH TO AUTOMATIC DETECTION of SUSPICIOUS INDIVIDUALS IN A CROWD" (2016). CUNY Academic Works.