A system and method for capturing a location based experience at an event including a plurality of mobile devices having a camera employed near a point of interest to capture random, crowdsourced images and associated metadata near said point of interest. In a preferred form, the images include depth camera information from prepositioned devices around the point of interest during the event. A network communicates images, depth information, and metadata to build a 3D model of the region, preferably with the location of contributors known. Users connect to this experience platform to view the 3D model from a user selected location and orientation and to participate in experiences with, for example, a social network.

An object recognition ingestion system is presented. The object ingestion system captures image data of objects, possibly in an uncontrolled setting. The image data is analyzed to determine if one or more a priori know canonical shape objects match the object represented in the image data. The canonical shape object also includes one or more reference PoVs indicating perspectives from which to analyze objects having the corresponding shape. An object ingestion engine combines the canonical shape object along with the image data to create a model of the object. The engine generates a desirable set of model PoVs from the reference PoVs, and then generates recognition descriptors from each of the model PoVs. The descriptors, image data, model PoVs, or other contextually relevant information are combined into key frame bundles having sufficient information to allow other computing devices to recognize the object at a later time.

Systems and methods for determining shrinkage of a commodity sold by a store are provided which may include: identifying data indicative of a volume of the commodity, associating a label with a container that holds the commodity, collecting the label information with a reader and transmitting collected label information to at least one server, automatically determining data characterizing the material in the container, associating the label information and the data characterizing the material in the container, and determining performance data related to the commodity by comparing the data indicative of the volume of the commodity supplied to the store, the data indicative of the volume of the commodity sold by the store, and the data characterizing the material in the container.

Systems and methods for determining shrinkage of a commodity sold by a store are provided which may include: identifying data indicative of a volume of the commodity, associating a label with a container that holds the commodity, collecting the label information with a reader and transmitting collected label information to at least one server, automatically determining data characterizing the material in the container, associating the label information and the data characterizing the material in the container, and determining performance data related to the commodity by comparing the data indicative of the volume of the commodity supplied to the store, the data indicative of the volume of the commodity sold by the store, and the data characterizing the material in the container.

Described is a system and method for enabling visual search for information. With each selection of an object included in an image, additional images that include visually similar objects are determined and presented to the user.

Textile matching using color and pattern recognition and methods of use are provided herein. An example method includes analyzing an image (305) of a first article of clothing to obtain color information and pattern information (205-230), comparing the color information and pattern information of the first article of clothing to color information and pattern information for a plurality of other articles of clothing (using Bayesian probability analysis to determine matched pairs, and providing a user with wardrobe suggestions using the matched pairs (705-740).

Textile matching using color and pattern recognition and methods of use are provided herein. An example method includes analyzing an image (305) of a first article of clothing to obtain color information and pattern information (205-230), comparing the color information and pattern information of the first article of clothing to color information and pattern information for a plurality of other articles of clothing (using Bayesian probability analysis to determine matched pairs, and providing a user with wardrobe suggestions using the matched pairs (705-740).

A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

A machine may be configured to perform image evaluation of images depicting items for sale and to provide recommendations for improving the images depicting the items to increase the sales of the items depicted in the images. For example, the machine accesses a result of a user behavior analysis. The machine receives an image of an item from a user device. The machine performs an image evaluation of the received image based on an analysis of the received image and the result of the user behavior analysis. The performing of the image evaluation may include determining a likelihood of a user engaging in a desired user behavior in relation to the received image. Then, the machine generates, based on the evaluation of the received image, an output that references the received image and indicates the likelihood of a user engaging in the desired behavior.

In one embodiment, a mobile-client system may receive user input from a first user to send a message to a plurality of second users. The user input may include an indication of the first user currently being located at a first geographic location, a time period for which the first geographic location of the first user is visible to the plurality of second users, and one or more content items shared by the first user. The mobile-client system may send the message to one or more second users selected from the plurality of second users. The mobile-client system may receive a notification referencing one or more of the selected second users to whom the message was sent. The notification may include an indication of one or more second geographic locations shared by the one or more of the selected second users.