ML texture analysis research in 2019 UF SSTP
June 2019 – August 2019
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This project was an attempt to design a hybrid model that incorporates a stackable, localized histogram layer on CNN for texture classification analysis. We used RBF (Radial Basis Function) for localized binning operation without binning contraints and evaluated its performance on KTH-TIPS 2b dataset.
The research was done in University of Florida as part of their 2019 SSTP (Student Science Training Program). UF SSTP is a seven week residential research program for that emphasizes in research participation with a UF faculty research scientist and his or her research team. I worked in Professor Alina Zare's Machine Learning and Sensing Lab and assisted with my mentor Joshua Peeples's research 'Histogram Layer for Texture Classification'. As this is an on-going research, I am not allowed to publish any code under research security agreement. The lead author Joshua Peeples expects to publish the final article in 2022 Spring along with my co-authorship.
The final deliverables including paper and poster can be accessed here
In the span of seven weeks, I modeled my own CNN model, performed experiments with different parameters, wrote a 5-page article, and gave a 10-minute presentation in front of SSTP student body. At the closing ceremony, my paper was awarded with 'Best Research Paper' in Computer Science division.
Gatorsense Features:
- KIM ACCEPTED TO COLUMBIA UNIVERSITY SEAS
- GATORSENSE HOSTS UF STUDENT SCIENCE TRAINING PROGRAM STUDENTS
Tyler Kim - taewook.kim@columbia.edu
Project Link: https://github.com/tylertaewook/sstp-hist-cnn
- [Dr. Joshua Peeples]
- [Dr. Alina Zare]
- UF Student Science Training Program
- UF Research Computing - HiPerGator