Question

My image dataset is from http://www.image-net.org. There are various synsets for different things like flora, fauna, persons, etc
I have to train a classifier which predicts 1 if the image belongs to floral synset and 0, otherwise.
Images belonging to floral synset can be viewed at http://www.image-net.org/explore, by clicking on the plant, flora, plant life option in the left pane.

These images include wide variety of flora – like trees, herbs, shrubs, flowers etc.
I am not able to figure out what features to use to train the classifier. There is a lot of greenery in these images, but there are many flower images, which don’t have much green component. Another feature is the shape of the leaves and the petals.

It would be helpful if anyone could suggest how to extract this shape feature and use it to train the classifier. Also suggest what other features could be used to train the classifier.
And after extracting features, which algorithm is to be used to train the classifier?

Answer 1

Not sure that shape information is the approach for the data set you have linked to.

Just having a quick glance at some of the images I have a few suggestions for classification:

1. Natural scenes rarely have straight lines – Line detection
2. You can discount scenes which have swathes of “unnatural” colour in them.
3. If you want to try something more advanced I would suggest that a hybrid between entropy/pattern recognition would form a good classifier as natural scenes have alot of both.
4. Attempting template-matching/shape matching for leaves/petals will break your heart – you need to use something much more generalised.

As for which classifier to use… I’d normally advise K-means initially and once you have some results determine if the extra effort to implement Bayes or a Neural Net would be worth it.

Hope this helps.

T.

Expanded:

“Unnatural Colors” could be highly saturated colours outside of the realms of greens and browns. They are good for detecting nature scenes as there should be ~50% of the scene in the green/brown spectrum even if a flower is at the center of it.

Additionally straight line detection should yield few results in nature scenes as straight edges are rare in nature. On a basic level generate an edge image, Threshold it and then search for line segments (pixels which approximate a straight line).

Entropy requires some Machine Vision knowledge. You would approach the scene by determining localised entropys and then histogramming the results here is a similar approach that you will have to use.

You would want to be advanced at Machine Vision if you are to attempt pattern recognition as this is a difficult subject and not something you can throw up in code sample. I would only attempt to implement these as a classifier once colour and edge information(lines) has been exhausted.

If this is a commercial application then a MV expert should be consulted. If this is a college assignment (unless it is a thesis) colour and edge/line information should be more than enough.