There are provided contextual image presentation systems and methods. Such a system includes a hardware processor and a system memory having stored therein a contextual image generator including a data mapping module and a data visualization module. The contextual image generator receives social media data describing social media posts, determines a geographical location corresponding to at least some of the social media posts, and identifies a subject category corresponding respectively to each of the social media posts. In addition, the contextual image generator groups the social media posts into social media collections based on at least one of the subject category and the geographical location corresponding to each social media post. The contextual image generator further generates a contextual image that visually associates at least one of the social media collections with the subject category and/or the geographical location used to group that social media collection.

The system and method described herein provide for a machine-learning model to automate determination of product attributes for a product based on images associated with the product. The product attributes can be used in online commerce to facilitate product selection by a customer. In accordance with this disclosure, the product attributes may be determined using machine-learning technology by processing images associated with the product (including product packaging). The machine-learning technology is trained using product-related vocabulary and potential attributes that can be discovered by analyzing the images associated with the product.

The system and method described herein provide for a machine-learning model to automate determination of product attributes for a product based on images associated with the product. The product attributes can be used in online commerce to facilitate product selection by a customer. In accordance with this disclosure, the product attributes may be determined using machine-learning technology by processing images associated with the product (including product packaging). The machine-learning technology is trained using product-related vocabulary and potential attributes that can be discovered by analyzing the images associated with the product.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium determining image relevance responsive to a search query. A method includes, for each resource in a plurality of resources, wherein each resource includes one or more images and text that is separate from each of the one or more images: determining, by a data processing apparatus, and from the text of the resource, resource topics described by the text of the resource. For each of the one or more images, processing the image to determine a set of image topics that describe topics to which content depicted in the image belongs. Determining, by the data process apparatus, one or more topic match scores, wherein each topic match score is a measure of relevance one or more of the image topics of the image to one or more of the resource topics of the resource.

In one aspect, an example method to be performed by a computing device includes (a) receiving a request to use a camera of the computing device; (b) in response to receiving the request, (i) using a microphone of the computing device to capture audio content and (ii) using the camera of the computing device to capture an image; (c) identifying reference audio content that has at least a threshold extent of similarity with the captured audio content; and (d) outputting an indication of the identified reference audio content while displaying the captured image.

Described is a system and method for enabling dynamic selection of a search input. For example, rather than having a static search input box, the search input may be dynamically positioned such that it encompasses a portion of displayed information. For example, a user may touch a touch-based display using two fingers to invoke the dynamic search input and then determine a size and a position of the dynamic search input by moving their fingers on the display. An image segment that includes a representation of the encompassed portion of the displayed information is generated and processed to determine an object represented in the portion of the displayed information. Additional images with visually similar representations of objects are then determined and presented to the user.

Examples of techniques for constructing, evaluating, and improving a search string for retrieving images are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method includes receiving, by a processing device, an item identifier. The method further includes retrieving, by the processing device, an item description based at least in part on the item identifier. The method further includes identifying, by the processing device, a tuple indicating a common item use based at least in part on the item description. The method further includes constructing, by the processing device, a search string based at least in part on the tuple. The method further includes retrieving, by the processing device, at least one image based at least in part on the search string.

A product image evaluation system. In embodiments, the system comprises or interacts with a product database comprising a product information record that comprises a product identifier and a product category for a product, and an image database comprising a plurality of candidate images for the product. In embodiments the image database can comprise images received from a plurality of different sources. The system can comprise a parameterized grouping engine configured to separate images into groups of similar images, an image selector configured to select one or more images from each group, and an image sorter configured to determine an order of the selected images. Embodiments can distill the superset of all available images to provide a set of images that are sufficiently different from each other and satisfy quality requirements. As a result, no images containing unique information are left behind, and images containing duplicate or irrelevant information are discarded.

Improved techniques for re-localizing Internet-of-Things (IOT) devices are disclosed herein. Sensor data digitally representing one or more condition(s) monitored by an IOT device is received. In response, a sensor readings map is accessed, where this map is associated with the IOT device. The map also digitally represents the IOT device's environment and includes data representative of a location of the IOT device within the environment. The map also includes data representative of the conditions monitored by the IOT device. Additionally, the map is updated by attaching the sensor data to the map. In some cases, a coverage map can also be computed. Both the sensors readings map and the coverage map can be automatically updated in response to the TOT device being re-localized.

A system accesses content structure that includes a first attribute table including a first list of attributes of a first object, and a first mapping including first attribute values. The first list of attributes of the first object also includes a quality attribute indicating a first quality. After a request to modify quality is received, the system searches a plurality of content structures for a suitable second content structure that comprises a second attribute table including a second list of attributes of a second object. The suitable content structure has another attribute that matches a corresponding attribute of the first list of attributes of the first object and a quality attribute indicating a second quality. The system modifies the first attribute table to include the second list of attributes of the second object. In this way content is generated that is of higher or lower quality than the original content.