The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 08, 2016

Filed:

Dec. 18, 2013
Applicant:

A9.com, Inc., Palo Alto, CA (US);

Inventors:

Simant Dube, Sunnyvale, CA (US);

Sunil Ramesh, San Jose, CA (US);

Xiaofan Lin, Palo Alto, CA (US);

Arnab Sanat Kumar Dhua, Mountain View, CA (US);

Colin Jon Taylor, Orinda, CA (US);

Jaishanker K. Pillai, Mountain View, CA (US);

Assignee:

A9.com, Inc., Palo Alto, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/46 (2006.01); G06F 17/30 (2006.01); G06K 9/00 (2006.01); G06T 9/00 (2006.01); H04N 19/90 (2014.01);
U.S. Cl.
CPC ...
G06F 17/30247 (2013.01); G06F 17/30271 (2013.01); G06K 9/00523 (2013.01); G06T 9/00 (2013.01); H04N 19/00945 (2013.01);
Abstract

Various embodiments may increase scalability of image representations stored in a database for use in image matching and retrieval. For example, a system providing image matching can obtain images of a number of inventory items, extract features from each image using a feature extraction algorithm, and transform the same into their feature descriptor representations. These feature descriptor representations can be subsequently stored and used to compare against query images submitted by users. Though the size of each feature descriptor representation isn't particularly large, the total number of these descriptors requires a substantial amount of storage space. Accordingly, feature descriptor representations are compressed to minimize storage and, in one example, machine learning can be used to compensate for information lost as a result of the compression.


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