Systems and methods for image processing
AssigneeHyperverge Inc.
InventorsVignesh Krishnakumar, Hariprasad Prayagai Sridharasingan, Adarsh Amarendra Tadimari, Saivenkatesh A
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Abstract
Efficient image processing systems and methods for image scene classification and similarity matching are disclosed. The image processing systems encompassed by this disclosure use a deep convolutional neural network to facilitate scene classification by recognizing the context of an image and thereby enabling searches for similar images. These methods and systems are scalable to a large set of images and achieve a higher performance compared to the current state of the art techniques.
Claims9
A method of performing scene classification of an image, the method comprising:
- receiving an image into a deep convolutional neural network (DCNN) comprising multiple layers, each layer having multiple nodes, the DCNN having been customized with adjusted weights at each layer;
- classifying, by the DCNN, based on the adjusted weights, the image into one or more intermediate output categories, the intermediate output categories being chosen from a set of pre-defined output categories, each of the pre-defined output categories being associated with a separate corresponding binary classifier and a separate corresponding scene classification;
- extracting, by the DCNN, based on the adjusted weights, one or more characteristic features of the image;
- and
- further classifying the image using at least the binary classifiers associated with the one or more intermediate output categories, based on the one or more characteristic features of the image, into the scene classifications corresponding to the one or more intermediate output categories or into a category that is not associated with any of the pre-defined output categories.
A system for scene classification of an image, the system comprising:
- a receiver unit for receiving an image;
- a base classifier, associated with said receiver unit, wherein the base classifier comprises a deep convolutional neural network (DCNN) comprising multiple layers, each layer having multiple nodes, the DCNN having been customized with adjusted weights at each layer, and wherein the DCNN, based on the adjusted weights, extracts one or more characteristic features of said image and classifies the image into one or more intermediate output categories, the intermediate output categories being chosen from a set of pre-defined output categories, each of the pre-defined output categories being associated with a separate scene classification;
- and
- a set of binary classifiers associated with the base classifier, each binary classifier being associated with a separate pre-defined output category of the set of pre-defined output categories, for further classifying the image, based on the one or more characteristic features of the image, into the scene classifications corresponding to the one or more intermediate output categories or into a category that is not associated with any of the pre-defined output categories.
Dependencies are inferred from the claim text.