ICIAP 2019 – Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval

Mohbat, Tooba Mukhtar, Numan Khurshid, and Murtaza Taj

International Conference on Image Analysis and Processing (ICAIP), Trento, Itlay, September 9-13, 2019

Abstract

Advancements in deep learning techniques caused a paradigm shift in feature extraction for image perception from handcrafted methods to deep methods. However, these deep features if learned through unsupervised methods bear large memory footprints and are prone to the curse of dimensionality. Traditional feature reduction schemes involving aggregation of these learned visual descriptors may lead to loss of essential information necessary for their obvious discrimination. Therefore, this research studies various feature reduction techniques for remote sensing image features. We also propose a deep discriminative network with dimensionality reduction (DAE-DR), exploiting stacked autoencoder based solution to abbreviate unsupervised features without significantly affecting their discriminative and regenerative characteristics. It is observed that the spatial dimensions encoded in the feature vector are more important than increasing the number of network filters for efficient image reconstruction. Validation of our approach has been tested for remote sensing image retrieval (RSIR) problem. Results demonstrate that our proposed network achieves 25 times reduction in feature size with only 0.8 times depletion of retrieval score.

Text Reference:

Mohbat, Tooba Mukhtar, Numan Khurshid, and Murtaza Taj,
"Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval
International Conference on Image Analysis and Processing (ICAIP), Trento, Itlay, September 9-13, 2019

Bibtex Reference:

@inproceedings{ResNet3DLeakyReLU2019,
   author   = "Mohbat and
               Tooba Mukhtar and
               Numan Khurshid and                               
               Murtaza Taj",
   title     = "Dimensionality Reduction Using Discriminative Autoencoders for Remote Sensing Image Retrieval",    
   booktitle = "International Conference on Image Analysis and Processing (ICAIP)",    
   month = "Sept ",
   year = "2019", }