Utilizing deep neural network-based model to identify visually similar digital images based on user-selected visual attributes
A deep neural network and digital image technology, which is applied in the field of identifying visually similar digital images based on the visual attributes selected by the user based on the deep neural network model, which can solve the problems of inability to customize images and inflexible characteristics
Pending Publication Date: 2019-11-26
ADOBE INC
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Due to their inflexible nature, these traditional systems generally cannot customize images to match the needs of the user, other than to search for a single image of a specific object
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[0024] One or more embodiments described herein provide benefits and address one or more of the foregoing or other problems in the art by providing a digital image matching system that utilizes a deep neural network-based model to accurately and flexibly identify Digital images that share visual attributes. For example, a digital image matching system can match digital images based on spatial selectivity, image composition, and / or object counting. Additionally, the digital image matching system can identify similar digital images based on a composite analysis of visual attributes from multiple query images.
[0025] To illustrate, in some embodiments, in addition to user selection of at least one of spatial selectivity, image composition, and / or object counting of the query digital image, the digital image matching system receives the query digital image for use in identifying Visually similar digital images. The digital image matching system also utilizes the trained deep n...
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The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a deep neural network-based model to identify similar digital images for query digital images. For example, the disclosed systems utilize a deep neural network-based model to analyze query digital images to generate deep neural network-based representations of the query digital images. In addition, the disclosed systems can generate results of visually-similar digital images for the query digital images based on comparing the deep neural network-based representations with representations of candidate digital images. Furthermore, the disclosed systems can identify visually similar digital images based on user defined attributes and image masks to emphasize specific attributes or portions of query digital images.
Description
Background technique [0001] Advances in computing devices and image analysis techniques have led to various innovations in identifying visually similar digital images. For example, image analysis systems are now capable of analyzing high-resolution digital images to identify objects within the images and searching terabytes of information stored in digital image databases to identify other digital images depicting the same or similar objects. [0002] However, despite these advances, conventional image analysis systems still suffer from many shortcomings, especially in terms of accuracy and flexibility in identifying similar digital images. For example, while traditional image analysis systems can identify the same object in two different digital images, these systems typically ignore other aspects of the image (eg, background, spatial arrangement of objects, and other visual attributes of the image). In fact, because traditional image analysis systems usually only rely on se...
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Login to View More IPC IPC(8): G06K9/62G06N3/04G06V20/00G06V10/764G06V10/771
CPCG06N3/045G06F18/22G06F18/214G06V20/00G06V10/454G06V10/82G06V10/764G06V10/761G06V10/771G06F18/211G06F18/2413G06F16/532G06F16/5854G06T2207/20081G06T7/75G06T2207/20084G06F18/41G06F18/2148
Inventor 林哲沈晓辉凌明扬张健明J·库恩B·巴特菲尔德
Owner ADOBE INC