A region proposal algorithm using texture similarity and perceptual grouping

Currently, the most prominent object recognition and image labeling techniques are based on the region proposal algorithms. One of the significant challenges of the region proposal algorithms is to achieve high Recall at high overlaps. This paper proposes a new region proposal algorithm using percep...

Full description

Saved in:
Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2023, Vol.14 (1), p.271-288
Main Authors: Taghizadeh, Maryam, Chalechale, Abdolah, Jannesari, Ali
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Intelligence
Deep learning
Engineering
Machine learning
Methods
Object recognition
Original Research
Proposals
Recall
Robotics and Automation
Similarity
Texture recognition
User Interfaces and Human Computer Interaction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Currently, the most prominent object recognition and image labeling techniques are based on the region proposal algorithms. One of the significant challenges of the region proposal algorithms is to achieve high Recall at high overlaps. This paper proposes a new region proposal algorithm using perceptual grouping to generate fitting regions to enhance the Recall at high overlaps. The proposed method comprises segmentation, region merging, based on texture descriptors, and similarity measurement. Furthermore, the algorithm introduces a hybrid approach to compute an efficient threshold. To fully assess the proposed algorithm, well-known metrics such as overlap and Recall are measured. Experimental results are reported on MSRC, VOC2007, VOC2012, and COCO 2017 datasets. The results are compared with segmentation algorithms, and several classical and deep learning-based region proposals. The evaluation results indicate a good improvement of the Recall at high overlaps, such as 0.8 and 0.9, with a reasonable number of regions.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-021-03296-5