Selecting a region of interest

What I had tried to do earlier before was select a region of interest close to the center of the wasp. While this showed improvement, it was not a well-defined procedure since finding the center would vary from wasp to wasp depending on the wings.

But now that we have segmented the wings off manually, and are proceeding with less difficult problem, we can elegantly choose a bounding box for our region of interest.

We do this by first creating a binary image, and then choosing the region with largest area (assumed to be the wasp body). This is accomplished via the connected components algorithm, or findContours in OpenCV. Once the blob is found, we simply return the "tightest" bounding box for that region.

Here are a few examples:

 

The images are cropped according to these bounding boxes, so there is significantly less data to process during runtime. These cropped images must all be rescaled to one size, so that the HOG algorithm can produce feature vectors of equal length. For now, we have chosen to rescale each cropped image to the average size of the rectangular bounding boxes.

After testing out the aforementioned pre-processing steps, we note a slight increase in performance for H-S histogram features, and a significant performance in HOG features.

Here are the results for Color Histograms:

With an overall accuracy of: 0.872283 (32.1/36.8) - Our highest yet again! Improved by about ~1%.

And here are the results for HOG features:

With an overall accuracy of: 0.817935 (30.1/36.8) - Showing over 10% improvement from last time!

Recognition accuracy is beginning to reach a point where it may be practical and preferred over manual recognition. I will continue looking into how to combine the two features into a single weighted feature vector.