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>>To improve CV we should focus on finding best ways of converting full resolution visual data to something of smaller volume in such way that important feature

by Dn_Ab 14y ago

>>To improve CV we should focus on finding best ways of converting full resolution visual data to something of smaller volume in such way that important features are preserved. You are exactly right! See Dictionary Learning, Random projection, compressive sensing. As for time, perhaps you are right I don't know. That question is: would a suitably written video trained classifier that preserved temporal features do better on image classification?

scotty79 14y ago
I think that it might be the case as I believe that there is a lot of information in movement (or in 3d that is made observable thanks to movement) than makes dividing objects into categories much easier task. There must be a lot of shortcuts that can be made thanks to the fact that objects are not something completely arbitrary but physical, usually solids and have to obey relatively simple rules of reality. Once this classifier is taught with reach data you can match newly observed object to one of the classes, relying only on very small amount of information, for example its 2d small resolution image. Seeing is in my opinion very similar to understanding language in sense that the information that is transferred, observed image or words heard are just small fuzzy fragments. Sender (speaker, or in case of vision, physical world) has rich model and recipient has rich model of all the things they can communicate about and the actual information passed, only indicates the parts of the underlying model to the recipient, that he should select and how he should modify them to get the message. Building usable model from small fuzzy fragments of information that are passed when recognizing image or hearing spoken words should be incredibly hard task and I think no biological brain could do that. I think that absorbing as much real information as possible at the time of training the classifier is absolutely crucial for achieving anything close to what humans or animals can do. From the techniques you mentioned, dictionary learning looks most awesome to me, and most applicable to CV.

by Dn_Ab 14y ago

scotty79 14y ago

I think that it might be the case as I believe that there is a lot of information in movement (or in 3d that is made observable thanks to movement) than makes dividing objects into categories much easier task. There must be a lot of shortcuts that can be made thanks to the fact that objects are not something completely arbitrary but physical, usually solids and have to obey relatively simple rules of reality. Once this classifier is taught with reach data you can match newly observed object to one of the classes, relying only on very small amount of information, for example its 2d small resolution image. Seeing is in my opinion very similar to understanding language in sense that the information that is transferred, observed image or words heard are just small fuzzy fragments. Sender (speaker, or in case of vision, physical world) has rich model and recipient has rich model of all the things they can communicate about and the actual information passed, only indicates the parts of the underlying model to the recipient, that he should select and how he should modify them to get the message. Building usable model from small fuzzy fragments of information that are passed when recognizing image or hearing spoken words should be incredibly hard task and I think no biological brain could do that. I think that absorbing as much real information as possible at the time of training the classifier is absolutely crucial for achieving anything close to what humans or animals can do. From the techniques you mentioned, dictionary learning looks most awesome to me, and most applicable to CV.