Oath Inc.

Verizon Patent and Licensing Inc.

Yahoo Assets LLC

Verizon Media Inc.

Yahoo Holdings, Inc.

Jenhao Hsiao

Jia Li

As provided herein, a domain model, corresponding to a domain of an image, may be merged with a pre-trained fundamental model to generate a trained fundamental model. The trained fundamental model may comprise a feature description of the image converted into a binary code. Responsive to a user submitting a search query, a coarse image search may be performed, using a search query binary code derived from the search query, to identify a candidate group, comprising one or more images, having binary codes corresponding to the search query binary code. A fine image search may be performed on the candidate group utilizing a search query feature description derived from the search query. The fine image search may be used to rank images within the candidate group based upon a similarity between the search query feature description and feature descriptions of the one or more images within the candidate group.

Image searching

Users may have a variety of photos, but lack a mechanism to organize the photos. For example, a user may desire to access a photo of a child in front of a national monument, but may be unable to locate the photo amongst the photos. Accordingly, a photo query may be generated utilizing a photo query interface populated with a face query attribute (e.g., a proxy icon, a photo of a face, etc.), a face position attribute (e.g., a position of the face query attribute), and/or a location query attribute (e.g., a background of a photo, such as a forest, a monument, etc.) using drag and drop functionality and/or any other query construction functionality. One or more photos having attributes corresponding to the photo query may be identified and provided to the user.

System and method for photo scene searching

Disclosed are systems and methods for improving interactions with and between computers in a search system supported by or configured with search servers or platforms. The systems interact to identify and retrieve data across platforms, which data can be used to improve the quality of results data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide a Deep Fast Search (DFS) that improves content search accuracy that executes independent of the search database, while achieving an increased content retrieval speed. The disclosed systems and methods employ two complementary deep feature searches: 1) a coarse deep feature search and a 2) fine deep feature search. Thus, the disclosed systems and methods employ a coarse-to-fine strategy that embodies the efficiency and cost effectiveness of the coarse deep feature search and the accuracy of the fine deep feature search.

System and method for improved server performance for a deep feature based coarse-to-fine fast search

Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate or support one or more processes and/or operations for one or more on-line recommendations, such as product-related recommendations, for example.

On-line product related recommendations

An approach for performing mobile visual search uses deep variant coding of images to reduce the amount of data transmitted from mobile devices to a search server and to provide more efficient indexing and searching on the search server. The amount of data used to represent an image varies depending upon the content of the image and is less than conventional fixed bit length hashing approaches. Denser regions of a feature space are represented by more encoding bits and sparser regions of the feature space are represented by fewer encoding bits, so that the overall number of encoding bits for an image feature is reduced. The approach generally involves determining a set of hash functions that provide deep hashing with more evenly-distributed hash buckets. One or more additional hash functions may be selectively generated for particular hash buckets that contain more than a specified number of images.

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Taipei, Taiwan

Jenhao Hsiao