Image clustering method, image clustering system, and image clustering server

An image clustering system, comprising a processor and memory configured to store program instructions, when executed by the processor, which cause the processor to perform operations comprising:

• extracting a Global Scene Semantic (GIST) feature of a first image and a GIST feature of a second image;
• obtaining an image fingerprint of the first image, based on the GIST feature of the first image and in conjunction with a Local Sensitive Hashing (LSH) algorithm and obtaining an image fingerprint of the second image, based on the GIST feature of the second image and in conjunction with the LSH algorithm, wherein obtaining an image fingerprint of an image, based on a GIST feature of the image and in conjunction with the LSH algorithm comprises:
• initializing an f-dimensional GIST feature vector and an f-bit binary as 0;
• for each dimension of GIST feature of the image;
• generating an f-bit signature for the GIST feature with a conventional hash algorithm;
• in a case that an i-th bit of the f-bit signature is 1, adding an i-th element of the f-dimensional GIST feature vector with a weight of the dimension of GIST feature;
• and in a case that the i-th bit of the f-bit signature is not 1, subtracting the weight of the dimension of GIST feature from the i-th element of the f-dimensional GIST feature vector, where 1≤i≤f;
• adding the f-dimensional GIST feature vectors for all dimensions of GIST feature of the image to obtain a first f-dimensional GIST feature vector;
• and
• outputting the f-bit binary as an image fingerprint of the image, wherein an i-th bit of the f-bit binary is 1 if an i-th element of the first f-dimensional GIST feature vector is larger than 0;
• and an i-th bit of the f-bit binary is 0 if an i-th element of the first f-dimensional GIST feature vector is not larger than 0;
• calculating a similarity between the first image and the second image, based on the image fingerprint of the first image and the image fingerprint of the second image;
• and
• classifying the first image and the second image as a same category of image, in the case that the similarity between the first image and the second image is larger than a predetermined similarity threshold.

A non-transient computer-readable storage medium storing computer executable instructions which, when run by a computer, cause the following steps to be executed:

• extracting a Global Scene Semantic (GIST) feature of a first image and a GIST feature of a second image;
• obtaining an image fingerprint of the first image, based on the GIST feature of the first image and in conjunction with a Local Sensitive Hashing (LSH) algorithm and obtaining an image fingerprint of the second image, based on the GIST feature of the second image and in conjunction with the LSH algorithm, wherein obtaining an image fingerprint of an image, based on a GIST feature of the image and in conjunction with the LSH algorithm comprises:
• initializing an f-dimensional GIST feature vector and an f-bit binary as 0;
• for each dimension of GIST feature of the image;
• generating an f-bit signature for the GIST feature with a conventional hash algorithm;
• in a case that an i-th bit of the f-bit signature is 1, adding an i-th element of the f-dimensional GIST feature vector with a weight of the dimension of GIST feature;
• and in a case that the i-th bit of the f-bit signature is not 1, subtracting the weight of the dimension of GIST feature from the i-th element of the f-dimensional GIST feature vector, where 1≤i≤f;
• adding the f-dimensional GIST feature vectors for all dimensions of GIST feature of the image to obtain a first f-dimensional GIST feature vector;
• and
• outputting the f-bit binary as an image fingerprint of the image, wherein an i-th bit of the f-bit binary is 1 if an i-th element of the first f-dimensional GIST feature vector is larger than 0;
• and an i-th bit of the f-bit binary is 0 if an i-th element of the first f-dimensional GIST feature vector is not larger than 0;
• calculating a similarity between the first image and the second image, based on the image fingerprint of the first image and the image fingerprint of the second image;
• and
• classifying the first image and the second image as a same category of image in the case that the similarity between the first image and the second image is larger than a predetermined similarity threshold.