Extraction of image feature data from images

Patent metadata

Field	Value
Publication number	US-9235859-B2
Application number	US-201414286594-A
Country	US
Kind code	B2
Filing date	May 23, 2014
Priority date	Sep 30, 2011
Publication date	Jan 12, 2016
Grant date	Jan 12, 2016

Abstract

An apparatus and method for obtaining image feature data of an image are disclosed herein. A color histogram of the image is extracted from the image, the extraction of the color histogram including performing one-dimensional sampling of pixels comprising the image in each of a first dimension of a color space, a second dimension of the color space, and a third dimension of the color space. An edge map corresponding to the image is analyzed to detect a pattern included in the image. In response to a confidence level of the pattern detection being below a pre-defined threshold, extracting from the image an orientation histogram of the image. And identify a dominant color of the image.

First claim

What is claimed is:

1. A method comprising:
generating a color histogram of an image by performing one-dimensional samplings of pixels of the image in each dimension of a multi-dimensional color space;
performing pattern detection on an edge map of the image;
generating an orientation histogram of the image in response to a confidence level of the pattern detection failing to transgress a threshold value, the generating of the orientation histogram being performed by a processor of a machine; and
determining a dominant color of the image.

2. The method of claim 1 , wherein:
the multi-dimensional color space includes a hue dimension, a saturation dimension, and a value dimension.

3. The method of claim 1 , wherein:
the multi-dimensional color space includes a first dimension, a second dimension, and a third dimension; and
the performing of the one-dimensional samplings includes sampling a first pixel among the pixels of the image in the first dimension in 24 bins, sampling the first pixel in the second dimension in 8 bins, and sampling the first pixel in the third dimension in 8 bins.

4. The method of claim 3 , wherein:
a second pixel among the pixels of the image has a saturation that fails to transgress a predetermined threshold; and
the performing of the one-dimensional samplings includes sampling the second pixel in a fourth dimension distinct from the first, second, and third dimensions of the multi-dimensional color space.

5. The method of claim 1 , wherein:
the multi-dimensional color space includes a first dimension, a second dimension, and a third dimension; and
the generating of the color histogram includes applying a first weight to a one-dimensional sampling of a first pixel among the pixels of the image in the first dimension, a second weight to a one-dimensional sampling of the first pixel in the second dimension, and a third weight to a one-dimensional sampling of the first pixel in the third dimension.

6. The method of claim 5 , wherein:
a second pixel among the pixels of the image has a saturation that fails to transgress a predetermined threshold; and
the generating of the color histogram includes applying a fourth weight to a one-dimensional sampling of the second pixel in a fourth dimension distinct from the first, second, and third dimensions of the multi-dimensional color space.

7. The method of claim 5 , wherein:
the generating of the color histogram includes combining a plurality of weighted one-dimensional samples of the pixels of the image to form the color histogram.

8. The method of claim 1 , wherein:
the determining of the dominant color of the image determines that the dominant color is present on more spatial area within a sample area of the image than other colors within the sample area of the image.

9. The method of claim 1 , wherein:
the determining of the dominant color of the image includes clustering colors of a sample area of the image and identifying a largest color cluster among a plurality of colors of the sample area of the image.

10. The method of claim 9 , wherein:
the multi-dimensional color space is a first multi-dimensional color space; and
the determining of the dominant color of the image includes clustering the colors of the sample area in accordance with a second multi-dimensional color space different from the first multi-dimensional color space.

11. The method of claim 10 , wherein:
the second multi-dimensional space is a nonlinear color space adopted by the International Commission on Illumination.

12. The method of claim 1 , wherein:
the performing of the one-dimensional samplings of the pixels includes performing uniform one-dimensional samplings of the pixels.

13. The method of claim 1 , wherein:
the performing of the pattern detection includes calculating a circularity value of a blob within the edge map of the image.

14. The method of claim 1 further comprising:
determining that the orientation histogram is indicative of the image having low spatial variation; and wherein
the generating of the color histogram is in response to the orientation histogram indicating that the image has low spatial variation.

15. The method of claim 1 further comprising:
determining that the orientation histogram is indicative of the image having high spatial variation;
determining a confidence score of an orientation of the image; and
indexing the image in accordance with the confidence score of the orientation of the image.

16. The method of claim 1 , wherein:
the image depicts a sellable item.

17. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
generating a color histogram of an image by performing one-dimensional samplings of pixels of the image in each dimension of a multi-dimensional color space;
performing pattern detection on an edge map of the image;
generating an orientation histogram of the image in response to a confidence level of the pattern detection failing to transgress a threshold value; and
determining a dominant color of the image.

18. The non-transitory machine-readable storage medium of claim 17 , wherein:
the generating of the orientation histogram includes:
calculating an x-derivative and a y-derivative of the edge map;
calculating a gradient and an orientation based on the x-derivative and the y-derivative; and
applying a weight to each edge pixel in the edge map to obtain the orientation histogram.

19. A system comprising:
one or more processors;
a color histogram module that configures at least one processor among the one or more processors to generate a color histogram of an image by performing one-dimensional samplings of pixels of the image in each dimension of a multi-dimensional color space;
a pattern module that configures at least one processor among the one or more processors to perform pattern detection on an edge map of the image;
an orientation histogram module that configures at least one processor among the one or more processors to generate an orientation histogram of the image in response to a confidence level of the pattern detection failing to transgress a threshold value; and
a dominant color module that configures at least one processor among the one or more processors to determine a dominant color of the image.

20. The system of claim 19 further comprising:
a capture module configured to receive the image from a device communicatively coupled to the system.

Assignees

• Ebay Inc

Inventors

• Bhardwaj Anurag
• Di Wei
• Hamid Muhammad Raffay
• Piramuthu Robinson
• Sundaresan Neelakantan

Classifications

• G06T7/90 Primary

  Determination of colour characteristics · CPC title

• G06F18/21

  Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation · CPC title

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title

• G06Q30/0629

  by pre-processing results, e.g. ranking or ordering results · CPC title

• G06T7/41

  based on statistical description of texture · CPC title