Hierarchical category classification scheme using multiple sets of fully-connected networks with a CNN based integrated circuit as feature extractor
First Claim
1. A system for classifying input data using a hierarchical category classification scheme comprising:
- a cellular neural networks (CNN) based integrated circuit being loaded with pre-trained filter coefficients of convolutional layers for extracting features out of an input data that belong to a particular domain; and
a multi-processor computing unit configured for using multiple hierarchically-ordered groups of pre-trained fully-connected networks (FCNs) in a hierarchical category classification scheme that contains a set of top level categories and each of the top level categories contains at least one set of subcategories, the extracted features being repeatedly processed through corresponding ones of the multiple hierarchically-ordered groups to identify the input data as a most probable category, the multiple hierarchically-ordered groups of FCNs containing a root level group configured for the set of top level categories and at least one next level group configured for the at least one set of subcategories.
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Abstract
CNN based integrated circuit is configured with a set of pre-trained filter coefficients or weights as a feature extractor of an input data. Multiple fully-connected networks (FCNs) are trained for use in a hierarchical category classification scheme. Each FCN is capable of classifying the input data via the extracted features in a specific level of the hierarchical category classification scheme. First, a root level FCN is used for classifying the input data among a set of top level categories. Then, a relevant next level FCN is used in conjunction with the same extracted features for further classifying the input data among a set of subcategories to the most probable category identified using the previous level FCN. Hierarchical category classification scheme continues for further detailed subcategories if desired.
91 Citations
19 Claims
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1. A system for classifying input data using a hierarchical category classification scheme comprising:
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a cellular neural networks (CNN) based integrated circuit being loaded with pre-trained filter coefficients of convolutional layers for extracting features out of an input data that belong to a particular domain; and a multi-processor computing unit configured for using multiple hierarchically-ordered groups of pre-trained fully-connected networks (FCNs) in a hierarchical category classification scheme that contains a set of top level categories and each of the top level categories contains at least one set of subcategories, the extracted features being repeatedly processed through corresponding ones of the multiple hierarchically-ordered groups to identify the input data as a most probable category, the multiple hierarchically-ordered groups of FCNs containing a root level group configured for the set of top level categories and at least one next level group configured for the at least one set of subcategories. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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Specification