A method of determining a pose of an image capture device includes capturing an image using an image capture device. The method also includes generating a data structure corresponding to the captured image. The method further includes comparing the data structure with a plurality of known data structures to identify a most similar known data structure. Moreover, the method includes reading metadata corresponding to the most similar known data structure to determine a pose of the image capture device.

A method of determining a pose of an image capture device includes capturing an image using an image capture device. The method also includes generating a data structure corresponding to the captured image. The method further includes comparing the data structure with a plurality of known data structures to identify a most similar known data structure. Moreover, the method includes reading metadata corresponding to the most similar known data structure to determine a pose of the image capture device.

Embodiments of the present disclosure relate to a method, a device and a computer readable medium for generating an image tag. According to the embodiments of the present disclosure, an index value of an image is determined based on contents of the image, similarities between a plurality of images is determined based on index values of the plurality of images, and thereby tags are generated for images. According to the embodiments of the present disclosure, images is further grouped depending on similarities between them.

Systems, methods, devices, media, and computer-readable instructions are described for local image tagging and processing in a resource-constrained environment such as a mobile device. In some embodiments, characteristics associated with images are used to determine whether to store content (e.g., images and video clips) as ephemeral content or non-ephemeral content. Based on the determination, the image is stored in a non-ephemeral camera roll storage of the mobile device, or an ephemeral local application storage. Additional storage operations such as encryption or backup copying may additionally be determined and performed based on the analysis of the content. In some embodiments, such images may be indexed, sorted, and searched based on the image tagging operations used to characterize the content.

A system and method for selecting a subset of images may include: obtaining a plurality of image files, each image file relating to a digital image of the plurality of digital images, each file including image data and metadata, the metadata including a first set of features, and a second set of features; clustering the plurality of images based on at least one of the first set of features to generate clusters of images; selecting a set of M largest clusters of images, wherein M is a positive integer; scoring the images of each of the selected clusters based on at least one of the second set of features; and selecting a set of N images with a highest score from the selected clusters, wherein N is a positive integer.

A system and method for selecting a subset of images may include: obtaining a plurality of image files, each image file relating to a digital image of the plurality of digital images, each file including image data and metadata, the metadata including a first set of features, and a second set of features; clustering the plurality of images based on at least one of the first set of features to generate clusters of images; selecting a set of M largest clusters of images, wherein M is a positive integer; scoring the images of each of the selected clusters based on at least one of the second set of features; and selecting a set of N images with a highest score from the selected clusters, wherein N is a positive integer.

Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

A method and apparatus for querying a historical street view, an electronic device and a computer readable storage medium are provided. The method may include: receiving a historical street view query request, and determining a target location point based on the historical street view query request; determining a first location point that is not more than a preset distance from the target location point and records a historical street view image; determining a second location point in the first location point, the second location point having a recording orientation of a street view image consistent with a recording orientation of a street view image of the target location point; and returning the historical street view image recorded at the second location point.

Techniques for generating an arbitrary view of an asset are disclosed. In some embodiments, arbitrary view generation includes storing a set of images of an asset, wherein each image comprising at least a subset of the set of images is rendered from a three-dimensional model of the asset, and generating an image comprising an arbitrary perspective of the asset at least in part by populating the image comprising the arbitrary perspective with pixels harvested from one or more images comprising the set of images.

Techniques for generating an arbitrary view of an asset are disclosed. In some embodiments, arbitrary view generation includes storing a set of images of an asset, wherein each image comprising at least a subset of the set of images is rendered from a three-dimensional model of the asset, and generating an image comprising an arbitrary perspective of the asset at least in part by populating the image comprising the arbitrary perspective with pixels harvested from one or more images comprising the set of images.