Article: Maximum entropy-based semi-supervised learning for automatic detection and recognition of objects using deep ConvNets Journal: International Journal of Computational Vision and Robotics (IJCVR) 2021 Vol.11 No.3 pp.328 - 356 Abstract: Object detection and localisation is one of the major research areas in computer vision that is growing very rapidly. Currently, there is a plethora of pre-trained models for object detection including YOLO, mask RCNN, RCNN, fast RCNN, multi-box, etc. In this paper, we proposed a new framework for object detection called 'maximum entropy-based semi-supervised learning for automatic detection and recognition of objects'. The main objective of this paper is to recognise objects from a number of visual object classes in a realistic scene simultaneously. The major operations of our proposed approach are preprocessing, localisation, segmentation and object detection. In the preprocessing, three processes, noise reduction, intensity normalisation, and morphology are considered. Then localisation and object segmentation is performed using maximum entropy in which optimal threshold is detected and in the end, object detection is performed using deep ConvNet. The performance of the proposed framework is evaluated using MATLAB-R2018b and it is compared with some previous state of the art techniques in terms of localisation error, detection and segmentation accuracy along with computation time. Inderscience Publishers - linking academia, business and industry through research

Title: Maximum entropy-based semi-supervised learning for automatic detection and recognition of objects using deep ConvNets

Authors: Vipul Sharma; Roohie Naaz Mir

Addresses: Department of Computer Science Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India ' Department of Computer Science Engineering, National Institute of Technology Srinagar, Jammu and Kashmir, India

Abstract: Object detection and localisation is one of the major research areas in computer vision that is growing very rapidly. Currently, there is a plethora of pre-trained models for object detection including YOLO, mask RCNN, RCNN, fast RCNN, multi-box, etc. In this paper, we proposed a new framework for object detection called 'maximum entropy-based semi-supervised learning for automatic detection and recognition of objects'. The main objective of this paper is to recognise objects from a number of visual object classes in a realistic scene simultaneously. The major operations of our proposed approach are preprocessing, localisation, segmentation and object detection. In the preprocessing, three processes, noise reduction, intensity normalisation, and morphology are considered. Then localisation and object segmentation is performed using maximum entropy in which optimal threshold is detected and in the end, object detection is performed using deep ConvNet. The performance of the proposed framework is evaluated using MATLAB-R2018b and it is compared with some previous state of the art techniques in terms of localisation error, detection and segmentation accuracy along with computation time.

Keywords: maximum entropy; object detection; weakly supervised learning; deep convolutional neural networks; segmentation and localisation.

DOI: 10.1504/IJCVR.2021.115165

International Journal of Computational Vision and Robotics, 2021 Vol.11 No.3, pp.328 - 356

Received: 23 Dec 2019
Accepted: 02 Jan 2020
Published online: 21 May 2021 *

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Title: Maximum entropy-based semi-supervised learning for automatic detection and recognition of objects using deep ConvNets

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