Hitachi Develops New Visual Search

Tech.Luver writes to tell us that Hitachi has developed a new visual search engine that can supposedly find similar images from within millions of video and picture data entries in around 1 second. "The technology assesses the similarity of images based on image characteristics presented as high-dimensional numeric information. The information is acquired by automatically detecting information regarding the images, such as color distribution and shapes."

Comments

similar to Video Google?

[gstone]

From the rather less than opaque description in the linked article, it seems that this works is a hierarchical extension to a system known as Video Google. This system detects two-dimensional features in every image of a video sequence. Then uses hierarchical clustering to group together "like" features together. The centres of these clusters are used as "visual words". Scenes from the original video can then be characterised by which of these visual words they contain.

Using these words, search engine style indices and techniques can be used to make searching -- by supplying an example image area which can have its words computed -- quite fast.

The key bottle neck here is the clustering stage: reducing the original input of typically hundreds of features per frame -- multiplied by 25 frames per second by minutes, or hours, of video -- to a much smaller set of clusters. It looks like the work in the linked article is using a modified clustering algorithm which does not require all of the data to be in memory at once.

The TRECVID project is a challenge style exercise where groups compete to provide the best search results for a given set of queries where the search material is hours of video.

Re:Hmmm.... robotics?

[kebes]

The field of evolutionary psychology is attempting to do just that: to deduce what algorithms are working in the human brain. One of the end-goals of such research is of course to be able to generate artificial versions of those algorithms. If you're interested in such things, Steven Pinker's "How the Mind Works" is a fantastic and accessible read. It describes how things like vision are optimized for environments we evolved from, and tend to fail when put in contrived situations (like optical illusions). It also tackles the "adaptive advantage" of having emotions, and so forth.

Of course, it's easier said than done to actually transplant a biological algorithm into a computer. Even when you figure out the basic strategy ("it seems to be a neural net that responds to edges in a visual image and passes shape information along to the next module"), it turns out that the details are difficult (millions of years of evolution have adjusted the exact "weighting factors" to very specific values!).

Re:Hmmm.... robotics?

[aragszxki]

Gödel's theorems have nothing to do with representing the human mind in any form. They cannot be applied to the human mind for the purposes of answering the question of strong AI. Basically, the only thing that Gödel's theorems do is carry the Liar's Paradox ("This sentence is false.") to the level of basic arithmetic. There is no magical process that proves or disproves anything about the human mind. The confusion stems from the fact that the mathematical terms "incomplete" and "inconsistent" seem to imply so much more when quoted in a non-mathematical context.

For anyone who is interested in reading further, Gödel's Theorem: An Incomplete Guide to Its Use and Abuse contains a thorough discussion of the issue.

I would like to believe that we will achieve strong AI one day. However, referencing Gödel's incompleteness theorems just because they sound appropriate at first glance does not give any argument scientific credibility.

Re:Hmmm.... robotics?

[mikael]

Here's a couple of articles I read:

Facts about the brain
Rods and Cones

There are around 125 millions rods and 6 million cones in each eye, with the percentages of each color/wavelength (red = 64%, green=32%, blue=2%)

No Sense

The human eye has 100 million neurons per per eye of five types, but there are only around 1 million neurons per optic nerve (arranged in bundles of 1000).