The present disclosure provides an image acquisition method, image acquisition device, electronic device, and computer-readable storage medium. The method includes: acquiring an image retrieval text input by a user and a screen display status of an image display device; determining retrieval intention information and a retrieval keyword according to the image retrieval text, wherein the retrieval intention information comprises information reflecting the user's retrieval intention, and the retrieval keyword comprises a keyword used to retrieve images; acquiring at least one candidate image according to the retrieval intention information and the retrieval keyword; and selecting a target image from the at least one candidate image according to the screen display status.

The present disclosure provides an image acquisition method, image acquisition device, electronic device, and computer-readable storage medium. The method includes: acquiring an image retrieval text input by a user and a screen display status of an image display device; determining retrieval intention information and a retrieval keyword according to the image retrieval text, wherein the retrieval intention information comprises information reflecting the user's retrieval intention, and the retrieval keyword comprises a keyword used to retrieve images; acquiring at least one candidate image according to the retrieval intention information and the retrieval keyword; and selecting a target image from the at least one candidate image according to the screen display status.

Techniques are disclosed herein for predicting and presenting to a user images corresponding to visual depictions of materials for a room or building that are pleasant to the user.

A computer-implemented method executed by at least one processor for detecting tattoos on a human body is presented. The method includes inputting a plurality of images into a tattoo detection module, selecting one or more images of the plurality of images including tattoos with at least three keypoints, the at least three keypoints having auxiliary information related to the tattoos, manually labeling tattoo locations in the plurality of images including tattoos to create labeled tattoo images, increasing a size of the labeled tattoo images identified to be below a predetermined threshold by padding a width and height of the labeled tattoo images, training two different tattoo detection deep learning models with the labeled tattoo images defining tattoo training data, and executing either the first tattoo detection deep learning model or the second tattoo detection deep learning model based on a performance of a general-purpose graphical processing unit.

A visual search engine is described herein. The visual search engine is configured to return information to a client computing device based upon a multimodal query received from the client computing device (wherein the multimodal query comprises an image and text). The visual search engine is further configured to interact with a user of the client computing device to disambiguate information retrieval intent of the user.

In implementations of determining user segmentation based on a photo library, a device maintains digital images in the photo library, as well as metadata associated with the digital images. The device includes a segmentation module implemented to determine characteristics about a user of the device by analysis of the metadata of the digital images. The segmentation module can determine a segmentation based on the characteristics determined about the user. The segmentation includes one or more segments that each represent a generalized aspect of the user, where a generalized aspect is attributable to multiple people and anonymity of the user is maintained. The segmentation module can associate an anonymous identifier with the segmentation effective to maintain the anonymity of the user and privacy of the metadata. The segmentation and the anonymous identifier can then be communicated to a marketing system that generates personalized marketing messages based on the segmentation.

In implementations of determining user segmentation based on a photo library, a device maintains digital images in the photo library, as well as metadata associated with the digital images. The device includes a segmentation module implemented to determine characteristics about a user of the device by analysis of the metadata of the digital images. The segmentation module can determine a segmentation based on the characteristics determined about the user. The segmentation includes one or more segments that each represent a generalized aspect of the user, where a generalized aspect is attributable to multiple people and anonymity of the user is maintained. The segmentation module can associate an anonymous identifier with the segmentation effective to maintain the anonymity of the user and privacy of the metadata. The segmentation and the anonymous identifier can then be communicated to a marketing system that generates personalized marketing messages based on the segmentation.

Descriptive data relating to at least a subset of a plurality of entities on a website is retrieved over a network. Endorsement data relating to the plurality of entities is retrieved from the website. A first set of probabilities is determined reflecting a probability that endorsements can be attributed to specific aspects. A second set of probabilities is determined reflecting a probability that terms can be attributed to aspects. Using the first set of probabilities and the second set of probabilities, a subset of the terms that are most probably associated with each entity are selected. Tags are then generated for each entity using the selected terms.

Methods, systems, and computer program products for generating metadata for image-based querying are provided herein. A computer-implemented method includes processing a query image against a database by applying a deep learning visual model to the query image and images contained within the database; retrieving a set of multiple images from the database based on the processing; identifying subsets of images among the set of multiple images by analyzing metadata attribute values of the set of multiple images and nearest neighbor distance values between the query image and the multiple images in the set; determining one or more items of metadata attributable to the query image by processing metadata of the subsets of images; and outputting, to a user, the items of metadata in response to the query image.

Methods, systems, and computer program products for generating metadata for image-based querying are provided herein. A computer-implemented method includes processing a query image against a database by applying a deep learning visual model to the query image and images contained within the database; retrieving a set of multiple images from the database based on the processing; identifying subsets of images among the set of multiple images by analyzing metadata attribute values of the set of multiple images and nearest neighbor distance values between the query image and the multiple images in the set; determining one or more items of metadata attributable to the query image by processing metadata of the subsets of images; and outputting, to a user, the items of metadata in response to the query image.