Invention Grant
- Patent Title: Object recognition using a convolutional neural network trained by principal component analysis and repeated spectral clustering
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Application No.: US15822982Application Date: 2017-11-27
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Publication No.: US10713563B2Publication Date: 2020-07-14
- Inventor: Barend Marius ter Haar Romenij , Samaneh Abbasi Sureshjani
- Applicant: Technische Universiteit Eindhoven
- Applicant Address: NL Eindhoven
- Assignee: Technische Universiteit Eindhoven
- Current Assignee: Technische Universiteit Eindhoven
- Current Assignee Address: NL Eindhoven
- Agency: Lumen Patent Firm
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06K9/62 ; G06N3/04 ; G06F17/16 ; G06K9/46

Abstract:
A method of object recognition trains a convolutional neural network (CNN) with a set of training images, then classifies an image of an object using the trained CNN. A first layer of the CNN is trained by generating a set of first convolutional filters from eigenvectors produced from linear principal component analysis of patches of the training images. The training of each of multiple hidden layers CNN includes generating a set of convolutional filters from a selected subset of eigenvectors produced from linear principal component analysis of patches of an affinity matrix constructed using a set of prior convolutional filters from a prior layer of the CNN, where the affinity matrix represents correlations of feature vectors associated with the prior layer. The last layer of the CNN is trained with a regular classifier by error back-propagation using the training images and labels associated with the training images.
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