Statistical max pooling with deep learning

A computer program product for image processing, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• receiving an image dataset having image data for a plurality of images and a group dataset having group information for each of the plurality of images;
• transforming the image dataset into a sparse vector by applying sparse coding and using the group information;
• dividing each of dimensions of the sparse vector into multiple groups with a matrix, the matrix being generated such that the matrix takes on a higher value when samples in a same group from among the multiple groups have same non-zero dimensions above a threshold amount of time and takes on a lower value compared to the higher value when the samples in the same group lack the same non-zero dimensions above the threshold amount of time;
• and
• transforming the sparse vector into a summation vector using the multiple groups.

A computing device, the computing device comprising:

• a processor and a memory operably coupled to the processor, configured to:
• receive an image dataset having image data for a plurality of images and a group dataset having group information for each of the plurality of images;
• transform the image dataset into a sparse vector by applying sparse coding and using the group information;
• divide each of dimensions of the sparse vector into multiple groups with a matrix, the matrix being generated such that the matrix takes on a higher value when samples in a same group from among the multiple groups have same nonzero dimensions above a threshold amount of time and takes on a lower value compared to the higher value when the samples in the same group lack the same non-zero dimensions above the threshold amount of time;
• and transform the sparse vector into a summation vector using the multiple groups.