Tuesday, January 31, 2017

Retraining the Helix classifier. Attempts and Hurdles

Over the past two weeks I've been working on retraining the Helix classifier to improve it's accuracy. Initially when I trained the classifier I used 20 positive samples and 10 negative samples. For the next trials I collected 40 positive samples and 500 negative samples. The positive samples came from the Collection E Notre Dame database. The negative samples were retrieved from the UIUC Image Database for Car Detection.

The cascade training was terrible with 40 positive and 500 negatives. ~6 minutes and 30 seconds in total time and the process terminated after 2 stages.

After a few attempts I wasn't able to successfully detect parts of the helix. I did multiple trials where I changed the ratio of positive to negative samples. (i.e. 40 positive, 250 negative / 40 positive 80 negative). The trial with 80 negatives was quicker, but only went through 1 stage of training and when tested no helixes were detected.

Some trials trained the classifier within seconds others were lengthy, over 5 minutes. I recently found a post on the forum site stackoverflow that provided suggestions on the sample sizes and properties that gave optimal results. The ideal settings had a positive to negative ratio of 2:1. Many people training haar classifiers generated 1000's of samples from a limited supply of positive images by applying small rotations and distortions to the original samples. These transformations can be performed using the opencv_createsamples utility. For each photo it's best to create 200 samples with this technique. Another thing I learned that will improve my training is to ensure my samples are monochrome and to scale the negatives to a size of 100 x 100. Negative images should be the same size or larger than positives. Currently the size of my negatives are 100 x 40. Much smaller than my positive samples. 

I will apply these techniques in my next trials of testing.

Friday, January 13, 2017

Walkthrough Meeting

Today we met together and had a walkthrough of making a lobule classifier. Morgan made a lot of progress and is now dividing her samples between narrow and wide lobules.

Howard University is holding for a research week event and our mentor, Dr. Washington advised us to apply and present our findings. We plan on showing the results of our work and the motivation behind the project. (An ear scheme that could recognize everyone (different groups/ races)
examining different data sets (Asian ears, black ears )

The submission requires an abstract and the deadline is February 26, 2017.

We are going to set up another meeting next Wednesday to start drafting our abstract. In the mean time Dr. Washington is having Morgan and I write a few paragraphs about our work at this point so we have somewhere to start our draft. We each have a responsibility to write 200 words for next meeting.

In addition, for next meeting I have to increase my image sample size from 20 to 100 samples to improve the accuracy of the helix classifier. As well as start on creating a classifier for the Tragus. Morgan plans on finishing her wide and narrow lobule classifier.

Important Dates:
HU Research Week February 26, 2017
Tapia scholarship is open General Tapia Scholarship Applicants: February 28, 2017
Grace Hopper Registration opens February