Canon Kabushiki Kaisha

Juwei Lu

Bradley Scott Denney

Yang Yang

Hung Khei Huang

Dariusz T Dusberger

Liyan Zhang

Daruisz T Dusberger

Systems and methods for learning a high-level visual vocabulary generate inter-visual-word relationships between a plurality of visual words based on visual word-label relationships, map the visual words to a vector space based on the inter-visual word relationships, and generate high-level visual words in the vector space.

Systems and methods for generating a high-level visual vocabulary

Systems, devices, and methods for generating an image representation obtain a set of low-level features from an image; generate a high-dimensional generative representation of the low-level features; generate a lower-dimensional representation of the low-level features based on the high-dimensional generative representation of the low-level features; generate classifier scores based on classifiers and on one or more of the high-dimensional generative representation and the lower-dimensional representation, wherein each classifier uses the one or more of the high-dimensional generative representation and the lower-dimensional representation as an input, and wherein each classifier is associated with a respective label; and generate a combined representation for the image based on the classifier scores and the lower-dimensional representation.

Devices, systems, and methods for learning a discriminant image representation

Systems and methods for generating visual words define initial inter-visual word relationships between a plurality of visual words; define visual word-image relationships between the plurality of visual words and a plurality of images; define inter-image relationships between the plurality of images; generate revised inter-visual word relationships in a vector space based on the initial inter-visual word relationships, the inter-image relationships, and the visual word-image relationships; and generate higher-level visual words in the vector space based on the revised inter-visual word relationships.

Systems and methods for feature fusion

Systems, devices, and methods for generating attribute scores obtain a plurality of object images; generate a respective first attribute score of a first attribute for each object image in the plurality of object images based on the object images; calculate a respective pairwise object-similarity measure for pairs of object images in the plurality of object images; and refine the first attribute score of an object image in the plurality of object images based at least in part on the attribute scores of other object images in the plurality of object images and on the object-similarity measures of the pairs of object images in the plurality of object images.

Devices, systems, and methods for visual-attribute refinement

Systems and methods for generating a visual vocabulary build a plurality of visual words via unsupervised learning on set of features of a given type; decompose one or more visual words to a collection of lower-dimensional buckets; generate labeled image representations based on the collection of lower dimensional buckets and labeled images, wherein labels associated with an image are associated with a respective representation of the image; and iteratively select a sub-collection of buckets from the collection of lower-dimensional buckets based on the labeled image representations, wherein bucket selection during any iteration after an initial iteration is based at least in part on feedback from previously selected buckets.

Systems and methods for creating a visual vocabulary

Systems and methods for summarizing a video assign frames in a video to at least one of two or more groups based on a topic, generate a respective first similitude measurement for the frames in a group relative to the other frames in the group based on a feature, rank the frames in a group relative to one or more other frames in the group based on the respective first similitude measurement of the respective frames, and select a frame from each group as a most-representative frame based on the respective rank of the frames in a group relative to the other frames in the group.

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Irvine, CA, United States of America

Juwei Lu