I\'ve been trying to teach myself some machine learning, and I wanted to ask wha

Question

I've been trying to teach myself some machine learning, and I wanted to ask what seems a simple question, but I've not been able to find any resources that explain the next step.

Let's say I am doing semi-supervised learning, and have a few hundred sparsely distributed feature extractors, trained on random 32x32 regions (the unsupervised part of the process).

I now want to take the larger images in my training set, and do some supervised learning based on the feature extractors I now have. In this case, multi-label classification.

The bit I'm not clear on is what I do with the full sized image from my training set:

Take random samples from it? -- seems like it would be pot luck if it picks an area needed to identify appropriate labels
Take overlapping tiles with a sliding window? -- seems like I'd end up with absurd dimensionality, since for each tile, I get a whole vector of features
Take adjacent tiles? -- dimensionality still nonsensical, and probably translation sensitive as well

It's a hypothetical example, but let's say my inputs are 800x600 photographs, i.e. the input is about 100-1000 times bigger than the samples I used in the unsupervised learning stage.

Explanation / Answer

I understand that you are trying to build a scene analysis algorithm based on patch based feature extractors. Apart from tbirdal's suggestions, take a look at literature on feature pooling, that is crucial for building higher level information from the output of low level features.

Take this paper for example:

A Theoretical Analysis of Feature Pooling in Visual Recognition

Related Questions

Navigate

• Browse (All)
• Browse I
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.