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公开(公告)号：US20240078576A1
公开(公告)日：2024-03-07
申请号：US18120421
申请日：2023-03-12
IPC分类号： G06Q30/0241 , G06N20/00 , G06T7/194 , G06T7/70
CPC分类号： G06Q30/0276 , G06N20/00 , G06T7/194 , G06T7/70
摘要： A method for an automated video generation from a set of digital images includes the step of obtaining the set of digital images. The set of digital images represent a specified object to be showcased in an automatically generated video. The method includes the step of implementing pose identification on each view of the specified object in the set of digital images. The method includes the step of implementing a background removal operation to set a consistent background to each digital image. The method includes the step of implementing an image resolution increase operation on each digital image. The method includes the step of implementing an attribute extraction operation on each digital image using a set of image classifiers. The set of image classifiers are run on each digital image to generate one or more textual tags. The one or more textual tags are integrated in the automatically generated video; The method includes the step of implementing an attention map generation. An attention map comprises a visualization of the specified object produced by a deep-learning algorithm that determines a most influential part of each digital image. A predicted tag specifying the most influential part each digital image, where each attention maps is used in the automatically generated video to zoom into specific areas of the object. The method includes the step of implementing an outfit generation of a collage of images of the specified object with other objects, wherein the collage of images is included in the automatically generated video to show various combinations of the specified object and other object. The method includes the step of generating a rendering of the automatically generated video comprising the set of digital images with the consistent background, an increased resolution, the one or more contextual tags, one or more zooms into specified areas a specified object and the collage of images.
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公开(公告)号：US11783408B2
公开(公告)日：2023-10-10
申请号：US16533767
申请日：2019-08-06
IPC分类号： G06F16/51 , G06N20/00 , G06V20/20 , G06V20/30 , G06Q30/0601 , G06F16/2457 , G06N5/04 , G06K9/62 , G06Q50/00
CPC分类号： G06Q30/0643 , G06F16/24578 , G06F16/51 , G06K9/626 , G06K9/6254 , G06K9/6257 , G06N5/042 , G06N20/00 , G06V20/20 , G06V20/30 , G06Q50/01
摘要： In one aspect, a computerized method of computer vision based dynamic universal fashion ontology fashion rating and recommendations includes the step of receiving one or more user-uploaded digital images. The method includes the step of implementing an image classifier on the one or more user-uploaded digital images, to classify a set of user-uploaded fashion content of the one or more user-upload digital images. The method includes the step of receiving a set of fashion rules input by a domain expert. The set of rules determine a set of apparel to match with the set of user-uploaded fashion content, generating a dynamic universal fashion ontology with the image classier and a text classier. The dynamic universal fashion ontology comprises an ontology of set of mutually exclusive attribute classes. The method includes the step of using the dynamic universal fashion ontology to train a specified machine learning based fashion classifications. The method includes the step of using an active learning pipeline to keep the universal fashion ontology up-to-date. The method includes the step of using graphical representation and game theory-based algorithm for outfit generation. The method includes the step of providing an automatic outfit generator, wherein the automatic outfit generator: based on the set of user-uploaded fashion content that is output by the image classifier, matches the set of user-uploaded fashion with a ranked set of apparel suggestions that are based on the set of fashion rules and the specified machine learning based fashion classifications, wherein the automatic outfit generator implements a greedy algorithm to determine the most optimal path in the specified machine learning based fashion classifications to generate each suggested piece of apparel in the ranked set of apparel suggestions. The method includes the step of, based on the highest ranked suggested piece of apparel in the ranked set of apparel suggestions, generating an outfit suggestion. The method includes the step of ranking based on lifestyle parameters including but not limited to weather, brand affinity, brand popularity and novelty of style.
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