The application discloses a method and device for personalized search of visual media. Semantic analysis is conducted on a visual media query text of a user to obtain visual semantic information, time information and/or location information. Semantic similarity matching is conducted on a result of the semantic analysis and attribute data of each visual medium within a specified search range to obtain a query similarity of the visual medium. The visual medium is an image or a video, and the attribute data include personalized visual semantic information, personalized time information and/or personalized location information. A corresponding visual media query result is generated based on the query similarity. By adopting the application, users are provided with visual media which is a result of a personalized.

The application discloses a method and device for personalized search of visual media. Semantic analysis is conducted on a visual media query text of a user to obtain visual semantic information, time information and/or location information. Semantic similarity matching is conducted on a result of the semantic analysis and attribute data of each visual medium within a specified search range to obtain a query similarity of the visual medium. The visual medium is an image or a video, and the attribute data include personalized visual semantic information, personalized time information and/or personalized location information. A corresponding visual media query result is generated based on the query similarity. By adopting the application, users are provided with visual media which is a result of a personalized.

An individual object identification system includes: an image acquisition processor configured to perform an image acquisition process to acquire an image of a subject acquired using imaging equipment; a feature point extraction processor that extracts a feature point from the image; a local feature amount calculation processor that calculates a local feature amount of the feature point; a local feature amount group classification processor that performs classification into a predetermined number of local feature amount groups; a global feature amount calculation processor that calculates a global feature amount based on each of the local feature amount groups; a searching target image registration processor that registers a plurality of images that are searching targets; a global feature amount registration processor that registers the global feature amount related to each of the registered images in a global feature amount registration unit; a narrowing processor that narrows down the plurality of registered images to registered images each having the global feature amount highly correlated with a global feature amount of an identification image; and a determination processor that compares the registered images as candidates with the identification image to determine the registered image having the largest number of corresponding points.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, modifying queries based on non-textual content. In one aspect, a method includes receiving, from a user device, a query including a plurality of terms; determining active non-textual data displayed in an application environment on the user device; determining, from the non-textual textual data, modification data for the query; generating a set of modified queries based on the query and the modification parameters; scoring the modified queries according to one or more scoring criteria; selecting one of the modified queries based on the scoring; and providing, to the user device, search results responsive to the selected modified query.

Implementations are described herein for leveraging digital media files retrieved and/or created by users to predict/determine topics of potential relevance to the users. In various implementations, digital media file(s) created and/or retrieved by a user with a client device may be applied as input across trained machine learning model(s), which in some cases are local to the client device, to generate output that indicates object(s) detected in the digital media file(s). Data indicative of the indicated object(s) may be provided to a remote computing system without providing the digital media file(s) themselves. In some implementations, information associated with the indicated object(s) may be retrieved and proactively output to the user. In some implementations, a frequency at which objects occur across a corpus of digital media files may be considered when determining a likelihood that a detected object is potentially relevant to a user.

A workpiece image search apparatus includes: a workpiece image deformation unit that generates a third workpiece image by deforming a second workpiece image so that a difference in workpiece shape between a first workpiece image and the second workpiece image becomes smaller, wherein the first workpiece image is obtained by projecting a first workpiece shape of a first workpiece on a two-dimensional plane, and the second workpiece image is obtained by projecting a second workpiece shape of a second workpiece on a two-dimensional plane; and a similarity calculation unit that calculates a similarity between the first workpiece shape and the second workpiece shape by comparing the third workpiece image with the first workpiece image.

In an object recognition apparatus, a storage unit stores a table in that a plurality of feature amounts are associated with each object having feature points of respective feature amounts. An object region detection unit detects object regions of a plurality of objects from an input image. A feature amount extraction unit extracts feature amounts of feature points from the input image. A refining unit refers to the table, and refines from all objects of recognition subjects to object candidates corresponding to the object regions based on feature amounts of feature points belonging to the object regions. A matching unit recognizes the plurality of objects by matching the feature points belonging to each of the object regions with feature points for each of the object candidates, and outputs a recognition result.

A transformer categorization architecture is applied to image and text data sets to determine a taxonomy for items in a large database of products. Aggregating recommendations from a multi-modal categorization process achieves a more accurate product classification with potentially less training. The system is implemented to support an e-commerce portal and user facilitated access to products for online purchases.

A distributed computing system (10) for locating product, comprising: a plurality of user devices (19); a product server (18) in communication over a network (15) with the plurality of user devices (19); and a product database (16) storing product data corresponding to a plurality of products, wherein each user device (19) is configured to: receive a user input to share an image with the product server (18); extract image data corresponding to the image from an image data source (13); and transmit the extracted image data to the product server (18), and wherein the product server (18) is configured to: in response to receiving the image data from one of the plurality of user devices (19), retrieve the image from an image source (12) based on the received image data; match the retrieved image with at least one of a plurality of products based on the product data stored in the product database (16); and transmit product data corresponding to the at least one product to the one of the plurality of user devices (19) in order to enable the one of the plurality of user devices (19) to display information relating to the at least one product.

An embodiment for media transit management is provided. The embodiment may include receiving one or more images and one or more pre-set configuration criteria regarding management of an image file. The embodiment may also include monitoring for an attempted sharing of the image file. The embodiment may further include in response to determining each object in the one or more images matches each object in the image file, identifying at least one other user who is attempting to share the image file. The embodiment may also include in response to determining the at least one other user is not authorized to share the image file, analyzing the one or more pre-set configuration criteria correlated with the image file. The embodiment may further include in response to determining the image file does not meet the one or more pre-set configuration criteria, prompting the participating user to respond to a notification.