A machine learning image processing system performs natural language processing (NLP) and auto-tagging for an image matching process. The system facilitates an interactive process, e.g., through a mobile application, to obtain an image and supplemental user input from a user to execute an image search. The supplemental user input may be provided from a user as speech or text, and NLP is performed on the supplemental user input to determine user intent and additional search attributes for the image search. Using the user intent and the additional search attributes, the system performs image matching on stored images that are tagged with attributes through an auto-tagging process.

A system, method and computer program product for accessing content based on an image. The method comprises comparing an image to a database of images, each of the images of the database being associated with at least one corresponding audio track, identifying those ones images of the database that correspond to the image, and identifying the at least one corresponding audio track that corresponds to the identified images. In one example aspect, the method also comprises presenting the audio track to a user. Corresponding metadata also can be presented. The images may be classified by, e.g., genre, musical album, concept, or the like, and, in cases where an input image is determined to belong to any such classes, audio content and/or metadata relating thereto are identified and presented to the user.

A system, method and computer program product for accessing content based on an image. The method comprises comparing an image to a database of images, each of the images of the database being associated with at least one corresponding audio track, identifying those ones images of the database that correspond to the image, and identifying the at least one corresponding audio track that corresponds to the identified images. In one example aspect, the method also comprises presenting the audio track to a user. Corresponding metadata also can be presented. The images may be classified by, e.g., genre, musical album, concept, or the like, and, in cases where an input image is determined to belong to any such classes, audio content and/or metadata relating thereto are identified and presented to the user.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

Approaches are described for shape-based graphics search. Each graphics object of a set of graphics objects is analyzed. The analyzing includes determining an outline of the graphics object from graphics data that forms the graphics object. The outline of the graphics object is sampled resulting in sampled points that capture the outline of the graphics object. A shape descriptor of the graphics object is determined which captures local and global geometric properties of the sampled points. Search results of a search query are determined based on a comparison between a shape descriptor of a user identified graphics object and the shape descriptor of at least one graphics object of the set of graphics objects. At least one of the search results can be presented on a user device associated with the search query.

Approaches are described for shape-based graphics search. Each graphics object of a set of graphics objects is analyzed. The analyzing includes determining an outline of the graphics object from graphics data that forms the graphics object. The outline of the graphics object is sampled resulting in sampled points that capture the outline of the graphics object. A shape descriptor of the graphics object is determined which captures local and global geometric properties of the sampled points. Search results of a search query are determined based on a comparison between a shape descriptor of a user identified graphics object and the shape descriptor of at least one graphics object of the set of graphics objects. At least one of the search results can be presented on a user device associated with the search query.

According to an embodiment, an image processing device includes one or more processors. The one or more processors are configured to: acquire an image; detect a first repeated pattern from the image; detect an object included in the first repeated pattern; and output the object as a second repeated pattern.

A method for image search, an electronic device, and a non-transitory computer-readable storage medium are provided. The method includes the following. Receive a query text. Process the query text to obtain a first multi-dimensional word vector. Perform, with a text gated network in a dual gate network, a first weighted operation on the first multi-dimensional word vector to obtain a second multi-dimensional word vector. Search for at least one target image according to the second multi-dimensional word vector and a second multi-dimensional visual vector for each image in an image file. The second multi-dimensional visual vector for each image is obtained by performing, with a visual gated network in the dual gate network, a second weighted operation on a first multi-dimensional visual vector for each image, and the second multi-dimensional word vector and the second multi-dimensional visual vector for each image are in a same space.

A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.