Computerized systems and methods are disclosed, including a computer system that executes software that may receive a geographic location having one or more coordinates of a structure, receive a validation of the structure location, and generate unmanned aircraft information based on the one or more coordinates of the validated location. The unmanned aircraft information may include an offset from the walls of the structure to direct an unmanned aircraft to fly an autonomous flight path offset from the walls, and camera control information to direct a camera of the unmanned aircraft to capture images of the walls at a predetermined time interval while the unmanned aircraft is flying the flight path. The computer system may receive images of the walls captured by the camera while the unmanned aircraft is flying the autonomous flight path and generate a structure report based at least in part on the images.

A device for organizing digital files is provided, wherein tagging data is applied to a sequence of image files previously captured by and received from an imaging device. A tagging image having tagging data therein is preferably provided before or after the sequence, or both. A text recognizing technology may be used to extract the tagging data from a tagging image. For example, if a tagging image is taken on a camera prior to a sequence of images, the device may extract tagging data from the tagging image and apply such tagging data to the sequence of image files. Preferably, the device provides for renaming of the image files utilizing the tagging data.

A system, which includes a mobile device and a support server, provides real-time notifications to a user whenever imaging satellites are positioned in the vicinity of the user, such that the imaging satellite is in a position to capture imagery of the user's location or a user-specified location. The user may access and purchase, via the mobile device application, available satellite imagery. In certain embodiments, the user may also be provided with the opportunity to issue, via the mobile device, a satellite tasking request such that a satellite will be tasked to capture imagery of a specific location.

A training data acquisition method and device, a server and a storage medium are provided. The training data acquisition method is applied to a classifier and includes the following steps: obtaining an image search target according to an input of a user; providing images to the user according to the image search target, to display the images; and selecting at least one image from the displayed images, and determining a target-classification pair as training data according to the at least one image; where the target-classification pair includes the image search target and an entity-based classification of the at least one image.

A training data acquisition method and device, a server and a storage medium are provided. The training data acquisition method is applied to a classifier and includes the following steps: obtaining an image search target according to an input of a user; providing images to the user according to the image search target, to display the images; and selecting at least one image from the displayed images, and determining a target-classification pair as training data according to the at least one image; where the target-classification pair includes the image search target and an entity-based classification of the at least one image.

In various example embodiments, a system and method for identifying top attributes are presented. Text content from an electronic document describing a product is extracted. A plurality of attributes associated with the product are identified using the extracted text content. A descriptor word proximate to each of the plurality of attributes in the extracted text content is detected. A weight is assigned to each descriptor word proximate to each of the plurality of attributes. The plurality of attributes are ranked based on the weight of the descriptor word proximate to each of the plurality of attributes. A predetermined number of top ranked attributes from the plurality of attributes are caused to be presented. Further, an image associated with each of the top ranked attributes is determined, the image being designated as a visual representation of the corresponding top ranked attribute. The image is caused to be displayed in association with the product on a user interface.

In various example embodiments, a system and method for identifying top attributes are presented. Text content from an electronic document describing a product is extracted. A plurality of attributes associated with the product are identified using the extracted text content. A descriptor word proximate to each of the plurality of attributes in the extracted text content is detected. A weight is assigned to each descriptor word proximate to each of the plurality of attributes. The plurality of attributes are ranked based on the weight of the descriptor word proximate to each of the plurality of attributes. A predetermined number of top ranked attributes from the plurality of attributes are caused to be presented. Further, an image associated with each of the top ranked attributes is determined, the image being designated as a visual representation of the corresponding top ranked attribute. The image is caused to be displayed in association with the product on a user interface.

Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.

Certain embodiments involve learning features of content items (e.g., images) based on web data and user behavior data. For example, a system determines latent factors from the content items based on data including a user's text query or keyword query for a content item and the user's interaction with the content items based on the query (e.g., a user's click on a content item resulting from a search using the text query). The system uses the latent factors to learn features of the content items. The system uses a previously learned feature of the content items for iterating the process of learning features of the content items to learn additional features of the content items, which improves the accuracy with which the system is used to learn other features of the content items.

Provided is a method of classifying, into an album, one or more image files stored in a terminal with a screen to which a touch screen is applied. Album/folder information included in the terminal is confirmed. An image file stored in the terminal is displayed on the screen of the terminal. It is determined whether a predetermined specific gesture is input with respect to the image file. When the specific gesture is input, it is determined whether the album/folder information is stored. When it is determined that the album/folder information is not stored, album/folder information is recommended. Both or one of the recommended album/folder information and the stored album/folder information is displayed on the screen of the terminal. It is determined to which album/folder information a drag is made among the album/folder information displayed on the screen of the terminal. The image file is moved to an album/folder corresponding to the album/folder information to which the drag has been made.