Histogram Layer for Texture Classification

ML texture analysis research in 2019 UF SSTP

June 2019 – August 2019
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Table of Contents

• About the Project
• Contact
• Acknowledgements

About The Project

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

Contact

Tyler Kim - taewook.kim@columbia.edu

Project Link: https://github.com/tylertaewook/sstp-hist-cnn

Acknowledgements

• [Dr. Joshua Peeples]
• [Dr. Alina Zare]
• UF Student Science Training Program
• UF Research Computing - HiPerGator