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Interwoven Patents Some Aspects Of Image Search
prostoalex writes "InterWoven patented locating and identifying image content via shapes, texture, color or resemblance to another image. No official word yet on whether the company thinks there are any infringers."
I believe Google just uses surrounding textual metadata from the web page to identify a likely candidate image... combined with a false positive approach where an image named or labeled money.gif on a page with 50% content about puppies.. probably the image isn't a puppy, while all other large file sized images most likely are puppies...
A fool throws a stone into a well and a thousand sages can not remove it.
The patent is more concerned with the method of converting the image into a manageable dataset that can be searched. So, it does not seem to rely on feature vectors at all. Moreover, this does look different than approaches I've seen to do the same thing. Fractal compression of images isn't terribly new, it's covered a bit in "Chaos and Fractals: New Frontiers in Science." However, I am not familiar with this method of classifying images based upon results from such a technique, this may very well be a novel development... dare I say, even worthy of a patent?
I have discovered a truly remarkable sig which this margin is too small to contain.
though, when those companies(and university projects and whatever that have been researching this) have been around researching this shit for the past 10 years.. they should have their bases under cover...
world was created 5 seconds before this post as it is.
Based on a short read of the article and the patent, I think that my former company (Audre, Inc., now eXtr@ct and Carnegie Mellon's Robotics Institute, among others, produced prior art as far back as the middle 1980's, back in the day before software and algorithms were patentable. I would go so far to say, at least at first glance, that some of the claims are superceded by convolution hardware dating to the 1960's at GE.
GE generated X:Y location & degree-of-match for an image regarding each of a set of simple image filters (rings of various radii and angular slits at 2 degree separation). This data was then cross correlated (some experiments used early neural nets, IIRC). They were successful at finding different types of features in aerial photography, such as farm, urban, water, grass, and forest.
The Audre Entity Recognition system used, among other things, input from a convolution/correlation system and a variety of other feature extraction methods, and used various means to build feature models from scanned engineering drawings, contour maps and other large format images. The system could produce a complete 3D terrain model from a simple contour map. The Visual Understanding Lab at CMU with which we worked also worked on using color features, more than Audre did. We even explored X-ray images, but scanning hardware of the time didn't have sufficient reliable gray scale capability.
A company in Denver or thereabouts was building systems using fractal decomposition of images as the fundamental data model for both display and recognition. They used a hexagonal cell model rather than the common rectangular one.
The patent is written in "patentese", so it'll take some study before I can be sure.
[Easter Egg: Check these movies (1, 2) and animated gif of ray tracing at 0.99c, by R. Thibadeau at CMU.]
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