On Optimal Quantization and its Effect on Anomaly Detection and Image Classification

This thesis presents the use of density estimation for performing data classification in different applications such as stream processing as well as image classification. The first half of this thesis presents a system that can process and analyze streaming data and extract the time frames that contain potential events of interest or anomalies without requiring any prior domain knowledge. The proposed method performs real time monitoring and mining of streaming data at multiple temporal scales simultaneously to maximize the probability of detection of anomalous events that span different lengths of time. The method does not assume the data segments containing anomalies belong to any particular distribution and therefore does not require prior domain knowledge. The system learns the evolution of normal behavior in streaming data and builds a model over time and uses it to determine whether the new incoming data fits that model. When analyzing streaming data, it is important for the algorithm to be fast with low computational complexity and therefore such aspects as well as the detection accuracy are studied and the results are presented. The algorithm is general and can be used for any type of streaming data. In the second half of this thesis, the feasibility of using density estimation in higher dimensions and in particular for visual descriptors is presented. A method for classifying images is proposed which uses density estimation to optimally quantize the feature space to generate a codebook used by a bag-of-features (BoF) image classifier. This thesis shows that the optimal smoothing calculation in density estimation can be used to systematically quantize the feature space to generate codebooks that can be used in image classification.

Author dc:creator

• Beigi, Mandis

• Electrical engineering
• Computer science

Language dc:language

English

DOI dc:identifier

https://doi.org/10.7916/D8891D1D

OAI identifier oai:identifier

oai:academiccommons.columbia.edu:10.7916/D8891D1D