Neural network image representation

Abstract

A method for representing an input image, the method including the steps of applying a trained neural network (NN) on the input image, selecting a plurality of feature maps, determining a location of each of the feature maps in an image space of the input image, defining a plurality of interest points of the input image, representing the input image as a graph according to the interest points and geometric relations between the interest points, and employing the graph for performing a visual task, the graph including a plurality of vertices and edges, and maintaining the data respective of the geometric relations, the feature maps being selected of an output of at least one selected layer of the trained NN according to values attributed to the feature maps by the trained NN, the interest points of the input image being defined based on the locations corresponding to the feature maps.

1 . A method for representing an input image, the method comprising the following procedures:
 applying a trained neural network on said input image;   selecting a plurality of feature maps of an output of at least one selected layer of said trained neural network according to values attributed to said plurality of feature maps by said trained neural network;   for each of said plurality of feature maps, determining a location corresponding thereto in an image space of said input image;   defining a plurality of interest points of said input image, based on said locations corresponding to said plurality of feature maps;   representing said input image as a graph according to said plurality of interest points and according to geometric relations between interest points of said plurality of interest points; and   employing said graph for performing a visual task,   wherein said graph comprises a plurality of vertices and edges; and   wherein said graph maintains data respective of said geometric relations between interest points.   
     
     
         2 . The method of  claim 1 , wherein said plurality of feature maps are selected according to a selected criterion of the list consisting of:
 said values attributed to said plurality of feature maps exceed a threshold;   said values attributed to said plurality of feature maps being the N highest values; and   said values attributed to said plurality of feature maps being in the upper P % of values,   wherein N and P are selected numerical values.   
     
     
         3 . The method of  claim 1 , wherein said procedure of defining said plurality of interest points comprises the sub-procedures of:
 combining said locations corresponding to said plurality of feature maps into at least one heat map; and   extracting said plurality of interest points from said at least one heat map.   
     
     
         4 . The method of  claim 3 , wherein each interest point of said plurality of interest points being an intensity peak of said at least one heat map. 
     
     
         5 . The method of  claim 3 , wherein each interest point of said plurality of interest points being a center of a region of said at least one heat map having high density of said locations corresponding to said plurality of feature maps, and wherein said region of said at least one heat map having high density of said locations being selected from the list consisting of:
 regions having density value exceeding a threshold;   N regions having the highest density values; and   regions in the upper P % of density values,   wherein N and P are selected numerical values.   
     
     
         6 . The method of  claim 1 , further comprising the procedure of associating each one of said plurality of interest points with a respective descriptor before said procedure of representing said input image as a graph.