Anomaly detection in multimedia datasets is a widely studied area. Yet, the concept drift challenge in data has been ignored or poorly handled by the majority of the anomaly detection frameworks. The state-of-the-art approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can drift from one class to another in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we systematically investigate the effect of concept drift on various detection models and propose a modified Adaptive Gaussian Mixture Model (AGMM) based framework for anomaly detection in multimedia data. In contrast to the baseline AGMM, the proposed extension of AGMM remembers the past for a longer period in order to handle the drift better. Extensive experimental analysis shows that the proposed model better handles the drift in data as compared with the baseline AGMM. Further, to facilitate research and comparison with the proposed framework, we contribute three multimedia datasets constituting faces as samples. The face samples of individuals correspond to the age difference of more than ten years to incorporate a longer temporal context.
翻译:多媒体数据集中的异常探测是一个广泛研究的领域,然而,大多数异常探测框架忽视或不当处理数据中的概念漂移挑战,而大多数异常探测框架忽视或处理不当。最先进的方法假定,培训和部署时间的数据分配情况将相同。然而,由于各种实际环境因素,数据分布可能出现漂移,或者在较晚的将来可能从一个舱到另一个舱之间漂移。因此,一个经过一次性培训的模式可能无法充分发挥作用。在本文件中,我们系统地调查概念漂移对各种探测模型的影响,并提议一个基于适应性高山混合模型(AGMM)的修改后多媒体数据异常探测框架。与AGMM的基线相反,拟议将AGMM的延长时间将回忆过去较长一段时间,以便更好地处理漂移问题。广泛的实验分析表明,拟议的模型比AGMMM更好地处理数据中的漂移问题。此外,为了便利研究和比较拟议的框架,我们提供了三个多媒体数据集作为样本。个人的脸样本与十多年以上的年龄差异相对,以纳入更长的时间背景。