Few-shot learning with background subtraction
| dc.contributor.advisor | Li, Longzhuang | |
| dc.contributor.author | Wang, Hui | |
| dc.contributor.committeeMember | Zhang, Ning | |
| dc.contributor.committeeMember | Kar, Dulal | |
| dc.creator.orcid | https://orcid.org/0000-0002-1201-718X | en_US |
| dc.date.accessioned | 2021-06-25T20:45:39Z | |
| dc.date.available | 2021-06-25T20:45:39Z | |
| dc.date.issued | 2020-12 | |
| dc.description.abstract | Few-shot learning for image classification aims to classify the image by only using few images as supporting samples. In the past several years, few-shot learning has achieved a huge improvement in image classification. In the recent work, such as meta-transferlearning (MTL) and Few-shot Adaptive Faster R-CNN have achieved a higher accuracy. In this paper, we are trying to combine three different methods together which are YOLOV2 model, Mask RCNN and our fewshot learning model. When a CNN wants to recognize animals in photos, there is a huge chance that even features that are supposed to represent trees will be encoded as belonging to those animals. Our main idea is to using YOLO algorithm, Mask RCNN and Opencv functions to reduce the noise and background as much as possible and keep our main object as it is. We would like to train our model using the image that only contain the object itself. We show that this approach is helpful to improve the accuracy in few-shot learning image classification. | en_US |
| dc.description.college | College of Science and Engineering | en_US |
| dc.description.department | Computing Sciences | en_US |
| dc.format.extent | 31 pages | en_US |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89703 | |
| dc.language.iso | en_US | en_US |
| dc.rights | This material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher. | en_US |
| dc.title | Few-shot learning with background subtraction | en_US |
| dc.type | Text | en_US |
| dc.type.genre | Thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | Texas A & M University--Corpus Christi | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |