Abstract: A virtual browsing experience may be implemented that allows a user to move a mobile device within a physical environment in order to control browser navigation to different items on an associated display. The virtual browsing experience improves the user's ability to recall where previously-viewed items are located in the virtual browsing environment. In some embodiments, a mobile device may determine its position and/or orientation in a physical environment, and when movement of the mobile device is detected, a user interface on an associated display may digitally navigate through multiple items according to the position and/or orientation of the mobile device. The position and orientation of the mobile device may be determined from position information or data obtained by a sensor device of the mobile device, and appropriate subsets of items can be determined for display based on detected movement of the mobile device.

Abstract: A virtual browsing experience may be implemented that allows a user to move a mobile device within a physical environment in order to control browser navigation to different items on an associated display. The virtual browsing experience improves the user's ability to recall where previously-viewed items are located in the virtual browsing environment. In some embodiments, a mobile device may determine its position and/or orientation in a physical environment, and when movement of the mobile device is detected, a user interface on an associated display may digitally navigate through multiple items according to the position and/or orientation of the mobile device. The position and orientation of the mobile device may be determined from position information or data obtained by a sensor device of the mobile device, and appropriate subsets of items can be determined for display based on detected movement of the mobile device.

Abstract: Approaches introduce a pre-processing and post-processing framework to a neural network-based approach to identify items represented in an image. For example, a classifier that is trained on several categories can be provided. An image that includes a representation of an item of interest is obtained. Rotated versions of the image are generated and each of a subset of the rotated images is analyzed to determine a probability that a respective image includes an instance of a particular category. The probabilities can be used to determine a probability distribution of output category data, and the data can be analyzed to select an image of the rotated versions of the image. Thereafter, a categorization tree can then be utilized, whereby for the item of interest represented the image, the category of the item can be determined. The determined category can be provided to an item retrieval algorithm to determine primary content for the item of interest.

Abstract: Approaches introduce a pre-processing and post-processing framework to a neural network-based approach to identify items represented in an image. For example, a classifier that is trained on several categories can be provided. An image that includes a representation of an item of interest is obtained. Rotated versions of the image are generated and each of a subset of the rotated images is analyzed to determine a probability that a respective image includes an instance of a particular category. The probabilities can be used to determine a probability distribution of output category data, and the data can be analyzed to select an image of the rotated versions of the image. Thereafter, a categorization tree can then be utilized, whereby for the item of interest represented the image, the category of the item can be determined. The determined category can be provided to an item retrieval algorithm to determine primary content for the item of interest.