Automatic handbag recognition using view-dependent CNNs
AssigneeFASHIONPHILE Group, LLC
InventorsSarah Davis, Ben Hemminger
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Abstract
A system and related methods for identifying characteristics of handbags is described. One method includes receiving one or more images of a handbag, eliminating all but select images from the one or more images of the handbag to obtain a grouping of one or more select images, the select images being those embodying a complete periphery and frontal view of the handbag. For each of the one or more select images, aligning feature-corresponding pixels with an image axis, comparing at least a portion of the one or more select images with a plurality of stored images, and determining characteristics of the handbag based on said comparing.
Claims12
A method for identifying characteristics of a handbag, the method comprising in any order:
- receiving one or more images of a handbag;
- eliminating all but select images from the one or more images of the handbag to obtain a grouping of one or more select images, the select images being those embodying a complete periphery and frontal view of the handbag;
- for each of the one or more select images, aligning feature-corresponding pixels with an image axis;
- comparing at least a portion of the one or more select images with a plurality of stored images;
- and
- determining said characteristics of the handbag based on said comparing;
- wherein the eliminating all but select images from the one or more images of the handbag comprises:
- executing a frontal view classifier, the frontal view classifier is trained by:
- collecting a first dataset, the first dataset comprises a plurality of images, each of the plurality of images comprises one of a plurality of handbags,
- labelling the plurality images that embody a complete periphery and frontal view as select,
- training a first model with the first dataset,
- collecting incorrect predictions from the first model, the collection of the incorrect predictions results in a second dataset, and
- training a second model with the first and second datasets, and
- identifying each of the one or more images that are select.
A computer system comprising:
- a processor;
- and
- a non-transitory computer-readable medium configured to store instructions, the instructions when executed by the processor cause the processor to perform steps comprising:
- receiving one or more images of a handbag;
- eliminating all but select images from the one or more images of the handbag to obtain a grouping of one or more select images, the select images being those embodying a complete periphery and frontal view of the handbag;
- for each of the one or more select images, aligning feature-corresponding pixels with an image axis;
- comparing at least a portion of the one or more select images with a plurality of stored images;
- and
- determining characteristics of the handbag based on said comparing;
- wherein the eliminating all but select images from the one or more images of the handbag comprises:
- executing a frontal view classifier, the frontal view classifier is trained by:
- collecting a first dataset, the first dataset comprises a plurality of images, each of the plurality of images comprises one of a plurality of handbags,
- labelling the plurality images that embody a complete periphery and frontal view as select,
- training a first model with the first dataset,
- collecting incorrect predictions from the first model, the collection of the incorrect predictions results in a second dataset, and
- training a second model with the first and second datasets, and
- identifying each of the one or more images that are select.
A non-transitory computer-readable medium configured to store instructions, the instructions when executed by one or more computers, cause the one or more computers to perform operations comprising:
- receiving one or more images of a handbag;
- eliminating all but select images from the one or more images of the handbag to obtain a grouping of one or more select images, the select images being those embodying a complete periphery and frontal view of the handbag;
- for each of the one or more select images, aligning feature-corresponding pixels with an image axis;
- comparing at least a portion of the one or more select images with a plurality of stored images;
- and
- determining characteristics of the handbag based on said comparing;
- wherein the eliminating all but select images from the one or more images of the handbag comprises:
- executing a frontal view classifier, the frontal view classifier is trained by:
- collecting a first dataset, the first dataset comprises a plurality of images, each of the plurality of images comprises one of a plurality of handbags,
- labelling the plurality images that embody a complete periphery and frontal view as select,
- training a first model with the first dataset,
- collecting incorrect predictions from the first model, the collection of the incorrect predictions results in a second dataset, and
- training a second model with the first and second datasets, and
- identifying each of the one or more images that are select.
Dependencies are inferred from the claim text.