Semaine 7 samedi

~ samedi ~

Donc... each image has a different number of keypoints when returned from SIFT, but the matrix passed to SVM still has to be the same length. How do we deal with that?

Using SIFT with SVM, paper from Tuebingen University [8]:
http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=61B12FAF72E0849976C4EB6095BB6A04?doi=10.1.1.88.4011&rep=rep1&type=pdf

So these guys above used something called the Bhattacharyya kernel, "trivially related to the better known Hellinger's distance," because these other guys used it http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.8610&rep=rep1&type=pdf , who also mentioned chisquared, but I don't know why they didn't use chisquared.

The guys who introduced the attribute-based classification used chisquared.

What's the advantage of each?

Chi-square:
http://www.okstate.edu/ag/agedcm4h/academic/aged5980a/5980/newpage28.htm
For unordered sets, nonparametric techniques are good:
"There are, however, certain advantages of nonparametric techniques such as Chi Square (X²). For one thing, nonparametric tests are usually much easier to compute. Another unique value of nonparametric procedures is that they can be used to treat data which have been measured on nominal (classificatory) scales. Such data cannot, on any logical basis, be ordered numerically, hence there is no possibility of using parametric statistical tests which require numerical data."

For... data of variable length, maybe? That is what I need to know:

"The Chi Square (X²) test is undoubtedly the most important and most used member of the nonparametric family of statistical tests. Chi Square is employed to test the difference between an actual sample and another hypothetical or previously established distribution such as that which may be expected due to chance or probability. Chi Square can also be used to test differences between two or more actual samples."

More about chi-square:
http://math.hws.edu/javamath/ryan/ChiSquare.html

Attribute-based classification:

Layer 1 is from image low-level feature to attributes, in this case, ingredients. Ground truth is labeled by the area of a certian attribute over the entire area of the food (excluding the backgrounds), so a fraction.

There's 13 ingredients, so train 13 attribute classifiers. Since ingredients in images overlap, one image may be used in multiple classifiers' training. For example, if a plate of pasta contains pasta, tomatoes, and meat, it will be used to train all 3 of pasta, tomato, and meat attribute classifiers.

These classifiers will be regressions, seems like, as opposed to discrete categories, because the answer to a image containing an ingredient is not just yes or no (binary), but how much of this attribute it contains. It is reasonable for plates of food because some peppers may be a garnish, instead of a full plate, which makes the difference between a stuffed pepper dish and roasted meat with pepper garnish.

Eh... but then, no they should be categories if chi-square is used, seems like...

Layer 2 is from attribute (the ingredient label) to cuisines.

Classifier will be SVM with chisquare kernel.
Features - SIFT seems good for a standard, I guess, but that discards color information, which is really important for food. Maybe also try RGB/HSV color histograms? Or it might be a good idea to build on top of Tingfan Wu's textons idea and try textons as a feature descriptor for attribute based classification.

Reference:
[8] Eichhorn, Jan, and Chapelle, Olivier. Object Categorization with SVM: Kernels for Local Features. http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=61B12FAF72E0849976C4EB6095BB6A04?doi=10.1.1.88.4011&rep=rep1&type=pdf