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New Algorithms Improve Image Search
bc90021 writes "Electrical engineers from UC San Diego are making progress on an image search engine that analyzes the images themselves. At the core of this Supervised Multiclass Labeling system is a set of simple yet powerful algorithms developed at UCSD. Once you train the system (the 'supervised' part), you can set it loose on a database of unlabeled images. The system calculates the probability that various objects it has been trained to recognize are present, and labels the images accordingly. After labeling, images can be retrieved via keyword searches. Accuracy of the UCSD system has outpaced that of other content-based image labeling and retrieval systems in the literature. One of the co-authors works at Google, where the researchers have access to image collections at the largest of scales."
I wish the article would mention more about why it is better than similar techniques that have been proposed in the past. (For example, http://luthuli.cs.uiuc.edu/~daf/papers/WAP-fin.pdf seems similar) For instance, where do they get their labels for the training data? A lot of people have tried using contextual words drawn from surrounding web text to limited success due to noise. It's also questionable how well their techniques can do if they need to pre-build a separate classification for each keyword. Finally, there are words that it seems impossible that they could ever distinguish. For example, 'man' vs. 'woman,' would be incredibly complicated for anything but a human. Where are the details? Oh yeah, it's a news story! Here's a link to the paper http://www.svcl.ucsd.edu/publications/journal/2007 /pami/pami07-semantics.pdf
The probability isn't zero or one because the system doesn't have perfect knowledge and the probability is with respect to what the system 'knows'. Probability here is estimated based on the limited representation of the algorithm, so it's saying that based on the things I've seen before with similar features that were labeled 'tiger', X% were labeled 'tiger.' I would then expect this new thing to be a 'tiger' with a probability of X. (Exactly how they come up with their estimate is a bit more complicated :)) Confidence is a reasonable way of describing what that probability represents, but it's correct to say probability.