Authors:
Leonardo Tadeu Lopes
1
;
Lucas Pascotti Valem
1
;
Daniel Carlos Guimarães Pedronette
1
;
Ivan Rizzo Guilherme
1
;
João Paulo Papa
2
;
Marcos Cleison Silva Santana
2
and
Danilo Colombo
3
Affiliations:
1
Department of Statistics, Applied Math. and Computing, UNESP - São Paulo State University, Rio Claro, SP, Brazil
;
2
School of Sciences, UNESP - São Paulo State University, Bauru, SP, Brazil
;
3
Cenpes, Petróleo Brasileiro S.A. - Petrobras, Rio de Janeiro, RJ, Brazil
Keyword(s):
Clustering, Unsupervised Manifold Learning, Anomaly Detection, Video Surveillance.
Abstract:
The huge increase in the amount of multimedia data available and the pressing need for organizing them in different categories, especially in scenarios where there are no labels available, makes data clustering an essential task in different scenarios. In this work, we present a novel clustering method based on an unsupervised manifold learning algorithm, in which a more effective similarity measure is computed by the manifold learning and used for clustering purposes. The proposed approach is applied to anomaly detection in videos and used in combination with different background segmentation methods to improve their effectiveness. An experimental evaluation is conducted on three different image datasets and one video dataset. The obtained results indicate superior accuracy in most clustering tasks when compared to the baselines. Results also demonstrate that the clustering step can improve the results of background subtraction approaches in the majority of cases.