Abstract:
This invention presents a universal framework for the discovery, understanding and matching of dress styles. In one embodiment, a computer-implemented method for building a universal dress style learner is disclosed, said method comprising: learning human skin models; detecting skin using the learned human skin models; collecting a set of dress images worn by a model; computing a set of style features based on the skin detected for at least one subset within the set of dress images; computing a set of clusters on the at least one subset of dress images based on at least one subset of the set of style features; validating the set of clusters for the at least one subset of style features; and computing a set of validated style features and a style basis.
Abstract:
The present disclosure describes a method and system called “Universal Learner (UL),” which provides a unified framework to understand multimedia signals. The UL utilizes the loosely annotated multimedia data on the Web, analyses it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.
Abstract:
A unified framework to understand multimedia signals utilizes the loosely annotated multimedia data on the Web, analysis it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.
Abstract:
“A Universal Learner (UL)” provides a unified framework to understand multimedia signals. The UL utilizes the loosely annotated multimedia data on the Web, analyses it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.
Abstract:
The present disclosure describes a method and system called “Universal Learner (UL),” which provides a unified framework to understand multimedia signals. The UL utilizes the loosely annotated multimedia data on the Web, analyses it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.
Abstract:
Methods and systems for discovering styles via color and pattern co-occurrence are disclosed. According to one embodiment, a computer-implemented method comprises collecting a set of fashion images, selecting at least one subset within the set of fashion images, the subset comprising at least one image containing a fashion item, and computing a set of segments by segmenting the at least one image into at least one dress segment. Color and pattern representations of the set of segments are computed by using a color analysis method and a pattern analysis method respectively. A graph is created wherein each graph node corresponds to one of a color representation or a pattern representation computed for the set of segments. Weights of edges between nodes of the graph indicate a degree of how the corresponding colors or patterns complement each other in a fashion sense.