Anzeige
Erschienen in:
2017 | OriginalPaper | Buchkapitel
Stacked Features Based CNN for Rotation Invariant Digit Classification
verfasst von : Ayushi Jain, Gorthi R. K. Sai Subrahmanyam, Deepak Mishra
Erschienen in: Pattern Recognition and Machine Intelligence
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Covolutional neural networks extract deep features from input image. The features are invariant to small distortions in the input, but are sensitive to rotations, which makes them inefficient to classify rotated images. We propose an architecture that requires training with images having digits at one orientation, but is able to classify rotated digits oriented at any angle. Our network is built such that it uses any simple unit of CNN by training it with single orientation images and uses it multiple times in testing to accomplish rotation invariant classification. By using CNNs trained with prominent features of images, we create a stacked architecture which gives adequately satisfactory classification accuracy. We demonstrate the architecture on handwritten digit classification and on the benchmark mnist-rot-12k. The introduced method is capable of roughly identifying the orientation of digit in an image.