Systems and methods for image processing

Abstract

Efficient image processing systems and methods for image scene classification and similarity matching are disclosed. The image processing systems encompassed by this disclosure use a deep convolutional neural network to facilitate scene classification by recognizing the context of an image and thereby enabling searches for similar images. These methods and systems are scalable to a large set of images and achieve a higher performance compared to the current state of the art techniques.

We claim: 
     
         1 - 9 . (canceled) 
     
     
         10 . A method providing images similar to a query image from within a set of images, the method comprising:
 Receiving a query image;   Providing said query image as an input to a deep convolutional neural network (DCNN), wherein the DCNN extracts a feature vector of said query image;   Reducing dimensionality of said feature vector to form a reduced dimensional feature vector;   Splitting the reduced dimensional feature vector into a plurality of query image segments; and   providing images similar to said query image based on a comparison between each of said query image segments and segments of the set of images.   
     
     
         11 . The method of  claim 10  wherein reducing dimensionality of said feature vector comprises of providing said feature vector as an input to an auto-encoder, wherein the auto-encoder processes said feature vector to form a reduced dimensional feature vector. 
     
     
         12 . The method of  claim 10  further comprising ranking the one or more similar images based on a distance between the query image and the similar images. 
     
     
         13 . The method of  claim 10  wherein the distance between the query image and the provided similar images is less than a predefined threshold. 
     
     
         14 . A system for providing images similar to a query image from within a set of images, the system comprising:
 An input module for receiving a query image;   A base classifier associated with said input module, wherein the base classifier comprises a deep convolutional neural network (DCNN), and wherein the DCNN extracts a feature vector of said query image;   A reduction module associated with said base classifier, for reducing dimensionality of said feature vector to form a reduced dimensional feature vector and splitting the reduced dimensional feature vector into a plurality of query image segments; and   A comparison module associated with said reduction module, for providing images similar to said query image based on a comparison between each of said query image segments and segments of the set of images.   
     
     
         15 . The system of  claim 14  further comprising a central database storing the set of images in the form of plurality of image segments, wherein each segment is stored in a hash table.