Regional Priority Based Anomaly Detection using Autoencoders
This is an incremental improvement for anomaly detection in images where object location and importance vary, such as in surveillance or medical imaging.
The paper tackles the problem of autoencoders' location invariance causing issues in anomaly detection when image items have varying importance and location matters, proposing a regional priority based autoencoder (RPAE) that uses a region proposal network to identify relevant areas and combines error scores from multiple decoders.
In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising. They have also been explored for anomaly detection in a few cases. However, due to location invariance property of convolutional neural network, autoencoders tend to learn from or search for learned features in the complete image. This creates issues when all the items in the image are not equally important and their location matters. For such cases, a semi supervised solution - regional priority based autoencoder (RPAE) has been proposed. In this model, similar to object detection models, a region proposal network identifies the relevant areas in the images as belonging to one of the predefined categories and then those bounding boxes are fed into appropriate decoder based on the category they belong to. Finally, the error scores from all the decoders are combined based on their importance to provide total reconstruction error.