SNOW CLASSIFIER CONTEXT WINDOW REDUCTION USING CLASS T-SCORES AND MEAN DIFFERENCES
First Claim
1. A method for determining important locations of a classifier, said method comprising:
- calculating a mean deviation and a standard deviation of every pixel location in a classifier with respect to positive and negative samples associated with said classifier;
ranking an importance of all pixel locations calculated with respect to said classifier based on a combination of t-scores and mean differences; and
selecting an adequate number of said locations to achieve a detection rate sufficient to said classifier for a particular application in order to determine which locations of said classifier are most significant in discrimination between class objects and non-class objects.
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Accused Products
Abstract
Methods, systems and processor-readable media for determining, post training, which locations of a classifier window are most significant in discriminating between class and non-class objects. The important locations can be determined by calculating the mean and standard deviation of every pixel location in the classifier context for both the positive and negative samples of the classifier. Using a combination of t-scores and mean differences, the importance of all pixel locations in the classifier score can be rank ordered. A sufficient number of pixel locations can then be selected to achieve a detection rate close enough to the full classifier for a particular application.
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Citations
20 Claims
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1. A method for determining important locations of a classifier, said method comprising:
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calculating a mean deviation and a standard deviation of every pixel location in a classifier with respect to positive and negative samples associated with said classifier; ranking an importance of all pixel locations calculated with respect to said classifier based on a combination of t-scores and mean differences; and selecting an adequate number of said locations to achieve a detection rate sufficient to said classifier for a particular application in order to determine which locations of said classifier are most significant in discrimination between class objects and non-class objects. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for determining important locations of a classifier, said system comprising:
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a processor; a data bus coupled to said processor; and a computer-usable medium embodying computer program code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for; calculating a mean deviation and a standard deviation of every pixel location in a classifier with respect to positive and negative samples associated with said classifier; ranking an importance of all pixel locations calculated with respect to said classifier based on a combination of t-scores and mean differences; and selecting an adequate number of said locations to achieve a detection rate sufficient to said classifier for a particular application in order to determine which locations of said classifier are most significant in discrimination between class objects and non-class objects. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A processor-readable medium storing code representing instructions to cause a process to determine important locations of a classifier, said code comprising code to:
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calculate a mean deviation and a standard deviation of every pixel location in a classifier with respect to positive and negative samples associated with said classifier; rank an importance of all pixel locations calculated with respect to said classifier based on a combination of t-scores and mean differences; and select an adequate number of said locations to achieve a detection rate sufficient to said classifier for a particular application in order to determine which locations of said classifier are most significant in discrimination between class objects and non-class objects. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification