Systems and methods for image classification

A non-transitory computer readable medium with a memory storing code instructions which, when executed by a processor, cause the processor to perform operations for classifying an input image, the operations comprising:

• tiling at least one region of interest of the input image into a set of tiles;
• for each tile, extracting a feature vector of the tile by applying a convolutional neural network, wherein a feature is a local descriptor of the tile;
• and
• processing the extracted feature vectors of the set of tiles to classify the input image.

A method for predicting a global score of an input image comprising:

• tiling at least a region of interest of the input image into a set of tiles;
• for each tile, extracting a feature vector of the tile by applying a convolutional neural network, wherein a feature is a local descriptor of the tile;
• and
• processing the extracted feature vectors of the set of tiles to obtain the global score of the input image.

A non-transitory computer readable medium with a memory storing code instructions which, when executed by a processor, cause the processor to perform operations for predicting a global score of an input image, the operations comprising:

• tiling at least a region of interest of the input image into a set of tiles;
• for each tile, extracting a feature vector of the tile by applying a convolutional neural network, wherein a feature is a local descriptor of the tile;
• and
• processing the extracted feature vectors of the set of tiles to obtain the global score of the input image.