Pixel Classification Project

Being interested in both machine learning and image processing, I built a pixel-level classifier, on a lark, whose output is the probability that any given pixel was from the class "foliage". The project, in summary, followed these steps:

Pixel Classification Project Steps
1. Collect images, each containing pixels from only one class of interest
2. Extract samples (small windows surrounding pixels of interest) from images
3. Calculate derived features
4. Train classifier to distinguish between "foliage" and "not foliage" classes
5. Apply learned classifier to test images containing pixels from both classes

The salient details of each step follow:


1. Data Acquisition
Thirty-five images of each class ("foliage" and "non-foliage") were acquired. All training images were downloaded from the World Wide Web, after being located by AllTheWeb (Pictures). All images needed to be of at least moderate resolution (about 640x480) to provide enough information to render accurate classifications.

For the "foliage" class, search terms such as "foliage", "leaves" and "grass" were used. Images in this class were screened visually to include images which contained foliage, and only foliage, meaning leaves, plant stalks and stems. Images containing any extraneous elements (colorful flowers, pets, children, wires, rocks, etc.) were excluded.

For the "non-foliage" class, arbitrary search terms were employed, which would likely find things other than plants, like "nail", "dancer", "hallway", etc. Images in this class were visually screened to include anything but foliage. Indoor scenes containing small potted plants, for instance, were excluded.

It would also have been possible to utilize training images with mixtures of "foliage" and "non-foliage" pixels instead, but this would have required determining pixel class (at least for homogeneous regions within images) by hand. I did try this in a similar, earlier experiment, and I will report that: 1. manual identification of pixels can be time-consuming; 2. region-by-region classing complicates the next step, sampling; and 3. I suspect that this approach is less accurate for identification of pixels near the edge of a homogeneous region (which are much harder to distinguish).


2. Data Sampling
One thousand samples were drawn from each image, for a grand total of 70,000 examples (= 2 classes x 35 images each x 1000 samples per image). Each sample was composed of the color information from a small window surrounding the pixel of interest at a random location within each image. The pixel at the center of the window was considered to be from "foliage" class, if it came from the "foliage" set of images, and "non-foliage" if it came from the "non-foliage" set of images.


3. Derived Features
In addition to the raw red, green and blue values, my program calculated several derived features from the image data. In all, 11 features were used:

1. red
2. green
3. blue
4. hue
5. saturation
6. value
7. hue2
8. edge detector: 5x5
9. edge detector: 9x9
10. edge detector: 13x13
11. edge detector: 21x21

Hue, saturation and value, taken together, are another way of representing colors and are easily calculated using MATLAB's rgb2hsv function. The "hue2" variable is a fuzzy function of the hue variable, using a curved plateau function (see my posting of Nov-16-2006, Fuzzy Logic In MATLAB Part 1), hand-tweaked to flag appropriate colors. The edge detectors perform a simple, quick edge detection process over varying window sizes, and are intended to capture texture information at different scales.

There is nothing special about the above list, and indeed the list of possible derived features is limited only by the imagination. The image processing field has delivered a wide array of filters and other such manipulations. Interested readers are urged to examine either of these texts:

Algorithms for Image Processing and Computer Vision, by Parker (ISBN: 0-471-14056-2)

Digital Image Processing, by Gonzalez and Woods (ISBN: 0-201-50803-6)


4. Classifier Construction
My foliage classifier is a logistic regression, only because logistic regression is quick to train, and it was handy, as glmfit in the Statistics Toolbox. Any other machine learning or statistical classifier (linear discriminant, neural network, k-nearest neighbors, etc.) could have been used instead.

As this was just a quick experiment, I didn't bother with rigorous testing, variable selection, etc. Still, results on test images were quite nice (see below), and flaws in the classifier could certainly be addressed through a more thorough and structured effort.


5. Classifier Recall
The finished model was executed on some of my own digital photographs, which contained both "foliage" and "non-foliage" elements. The result appears below.

Overgrown Beams: Original Image
Overgrown Beams: Foliage Detection

Monarch Butterfly: Original Image
Monarch Butterfly: Foliage Detection

Potted Plant: Original Image
Potted Plant: Foliage Detection


Conclusion
I find working with image data to be particularly satisfying since the result is something one can actually look at. Images contain a great deal of data, both in terms of rich structure and in the sheer number of pixels. Even inexpensive digital cameras will deliver several million pixels per image, so consider image processing as a test-bed application for modeling experiments.

This was just a toy project, so it is hardly the last word in foliage detectors and weaknesses in the model should be evident in the images above. I strongly encourage readers to explore this field and improve on what has been presented. Consider training on other classes, like "skin", "people", "sky", "brickface", etc. I would be very interested in hearing from any readers who have results to share. Good luck!


Note on Handling Images in MATLAB
Even without toolboxes, MATLAB provides several tools for dealing with images, such as imread, which is used to load images. Most color images are loaded into MATLAB as 3-dimensional arrays, and are accessed as Image(VerticalCoordinate,HorizontalCoordinate,ColorPlane). The color channels are numbered: red (1), green (2) and blue (3), so Image(:,:,2) is just the green color plane of the image.


Related Work
One interesting work on the subject of pixel classification (skin detection) is Skin Detection, by Jennifer Wortman. While the classifier in this document is based only on color and is constructed by hand, the reader should find some insights on pixel classification.

See Also

Feb-02-2007 posting, Pixel Classificiation Project: Response

Mar-23-2007 posting, Two Bits of Code