EXTENDING DATA-DRIVEN DETECTION TO THE PREDICTION OF OBJECT PART LOCATIONS
First Claim
1. A method for detecting an object part location, said method comprising:
- defining a relevance value between a configuration of a plurality of parts and a set of training images annotated via an annotating object;
applying a similarity learning algorithm with respect to said plurality of parts to obtain a learned similarity function based on said relevance value; and
measuring a similarity between a new image and said set of training images utilizing said learned similarity function in order to obtain a neighbor image and predict a visible and/or a non-visible part location with respect to said new image based on said neighbor image.
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Abstract
Methods and systems for detecting an object part location based on an extended date-driven detection. A specific relevance value between configurations of parts with respect to a set of training images annotated with annotating objects can be defined. A similarity learning algorithm can be applied with respect to the parts to obtain a similarity function based on the similarity between the part configurations. The similarity learning algorithm receives a set of positive pair having similar part configuration and a negative pair having different configuration and returns the similarity function that tends to assign a high score to new positive pair and a low score to negative pairs. A similarity between a new image and the training images can be measured utilizing the learned similarity function to obtain a neighbor image and a visible and/or non-visible part location with respect to the image can be predicted based on the neighbor image.
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Citations
20 Claims
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1. A method for detecting an object part location, said method comprising:
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defining a relevance value between a configuration of a plurality of parts and a set of training images annotated via an annotating object; applying a similarity learning algorithm with respect to said plurality of parts to obtain a learned similarity function based on said relevance value; and measuring a similarity between a new image and said set of training images utilizing said learned similarity function in order to obtain a neighbor image and predict a visible and/or a non-visible part location with respect to said new image based on said neighbor image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for detecting an object part location, said system comprising:
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a processor; and a computer-usable medium embodying computer program code, said computer-usable medium capable of communicating with the processor, said computer program code comprising instructions executable by said processor and configured for; defining a relevance value between a configuration of a plurality of parts and a set of training images annotated via an annotating object; applying a similarity learning algorithm with respect to said plurality of parts to obtain a learned similarity function based on said relevance value; and measuring a similarity between a new image and said set of training images utilizing said learned similarity function in order to obtain a neighbour image and predict a visible and/or a non-visible part location with respect to said new image based on said neighbour image. - View Dependent Claims (12, 13, 14, 15)
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16. A processor-readable medium storing code representing instructions to cause a process for detecting an object part location, said code comprising code to:
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define a relevance value between a configuration of a plurality of parts and a set of training images annotated via an annotating object; apply a similarity learning algorithm with respect to said plurality of parts to obtain a learned similarity function based on said relevance value; and measure a similarity between a new image and said set of training images utilizing said learned similarity function in order to obtain a neighbour image and predict a visible and/or a non-visible part location with respect to said new image based on said neighbour image. - View Dependent Claims (17, 18, 19, 20)
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Specification