Object recognition

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 obtaine...

Full description

Saved in:
Bibliographic Details
Main Authors Ravichandran, Avinash Aghoram, Jain, Anshul Kumar, Rybakov, Oleg, Nambiar, Rakesh Madhavan, Helmer, Scott Daniel, Benitez, Matias Omar Gregorio, Bhotika, Rahul, Jia, Junxiong
Format Patent
LanguageEnglish
Published 13.08.2019
Subjects
CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
Online AccessGet full text

Cover

More Information
Summary: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. This information also can be used to determine recommendations, advertising, or other supplemental content, within a specific category, to be displayed with the primary content.
Bibliography:Application Number: US201715789789