Image feature extraction using entropy-based analysis
AssigneeHewlett Packard Enterprise Development LP
InventorsSatish Kumar Mopur, Vijaya Sharvani Hindnavis, Sridhar Balachandriah
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Abstract
Systems and methods are provided for feature extraction from visual inputs. Examples include calculating entropy information for a plurality of regions of the visual input and computing a hyperparameter for a clustering algorithm based on the entropy information. The hyperparameter is then used by a clustering algorithm to segment the visual input into a plurality of clusters. A feature set of the visual input is extracted based on the plurality of clusters.
Claims19
A method for feature extraction from an image, comprising:
- generating a plurality of regions of an image, the plurality of regions defining a first region of the image and iteratively expanding the first region for a subset of the plurality of regions, wherein the plurality of regions comprises the first region and the subset of the plurality of regions,
- calculating entropy information for the plurality of regions of the image, wherein calculating the entropy information comprises calculating an entropy value for each of the plurality of regions;
- computing a hyperparameter for a clustering algorithm based on the entropy information;
- segmenting the image into a plurality of clusters using the clustering algorithm and the hyperparameter;
- and
- extracting a feature set of the image based on the plurality of clusters.
A system for feature extraction, comprising:
- a memory storing instructions;
- and
- at least one processor coupled to the memory and configure to execute the instructions to:
- calculate entropy information by determining an entropy value for each of a plurality of regions of an image;
- selecting a maximum entropy value of the calculated entropy values;
- determine a hyperparameter for a clustering algorithm based on the entropy information, wherein the hyperparameter is determined based on the maximum entropy value;
- segmenting the image into a plurality of clusters using the clustering algorithm and the hyperparameter;
- and
- extracting a feature set of the image based on the plurality of clusters.
A non-transitory machine readable medium that, when executed by at least one processor of a computing system, causes the computing system to perform a method comprising:
- performing a plurality of entropy scans of an image, wherein the plurality of entropy scans comprises a first entropy scan and a second entropy scan, wherein the first entropy scan and the second entropy scan are performed in parallel, the plurality of entropy scans providing entropy information by determining an entropy value for each of a plurality of regions of the image;
- and
- deriving a hyperparameter for a clustering algorithm based on the entropy information and extract a feature set of the image by applying the clustering algorithm to the image using the derived hyperparameter.
A method for feature extraction from an image, comprising:
- calculating entropy information for a plurality of regions of the image;
- computing a kernel bandwidth for a clustering algorithm based on the entropy information, wherein the clustering algorithm is based on a Mean Shift clustering algorithm;
- segmenting the image into a plurality of clusters using the clustering algorithm and the kernel bandwidth;
- and extracting a feature set of the image based on the plurality of clusters.
A non-transitory machine readable medium that, when executed by at least one processor of a computing system, causes the computing system to perform a method comprising:
- generating a plurality of regions of an image by defining a first region of the image and iteratively expanding the first region to a subset of the plurality of regions, wherein the plurality of regions comprises the first region and the subset of the plurality of regions;
- performing a plurality of entropy scans of the image, the plurality of entropy scans providing entropy information by determining an entropy value for each of the plurality of regions of the image;
- and
- deriving a hyperparameter for a clustering algorithm based on the entropy information and extract a feature set of the image by applying the clustering algorithm to the image using the derived hyperparameter.
Dependencies are inferred from the claim text.