Collaborative Multi-Resolution MSER and Faster RCNN (MRMSER-FRCNN) Model for Improved Object Retrieval of Poor Resolution Images

Object detection and retrieval is an active area of research. This paper proposes a collaborative approach that is based on multi-resolution maximally stable extreme regions (MRMSER) and faster region-based convolutional neural network (FRCNN) suitable for efficient object detection and retrieval of poor resolution images. The proposed method focuses on improving the retrieval accuracy of object detection and retrieval. The proposed collaborative model overcomes the problems in a faster RCNN model by making use of multi-resolution MSER. Two different datasets were used on the proposed system. A vehicle dataset contains three classes of vehicles and the Oxford building dataset with 11 different landmarks. The proposed MRMSER-FRCNN method gives a retrieval accuracy 84.48% on Oxford 5k building dataset and 92.66% on vehicle dataset. Experimental results show that the proposed collaborative approach outperform the faster RCNN model for poor-resolution conditioned query images.

@article{C2021,
  title     = {Collaborative Multi-Resolution MSER and Faster RCNN (MRMSER-FRCNN) Model for Improved Object Retrieval of Poor Resolution Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Amitha I C and N S Sreekanth and N K Narayanan},
  doi       = {10.14569/IJACSA.2021.0121270},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121270}
}