IMAGE SEMANTIC CLOTHING ATTRIBUTE

• US 20130236065A1
• Filed: 03/12/2012
• Published: 09/12/2013
• Est. Priority Date: 03/12/2012
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Zhang, Tong, Wang, Xianwang
• Application Number:
  US13/417,412
• Publication Number:
  US 20130236065A1
• Time in Patent Office: Days
• US Class Current: 382/111
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06V 10/75 Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49

Abstract

Embodiments disclosed herein relate to a semantic clothing attribute in an image. A probability of a semantic clothing attribute in an image may be determined. The probability of the semantic clothing attribute may be determined based on a comparison of the image to an image feature associated with the semantic clothing attribute.

Claims

1. A machine-readable non-transitory storage medium comprising instructions executable by a processor to:

determine the probability of the presence of semantic clothing attributes in a first image and a second image;

determine a level of similarity between the first image and the second image based on a comparison of the probability of the semantic clothing attributes in the first image and the second image; and

output information about the similarity level between the first and second image.

2. The machine-readable non-transitory storage medium of claim 1, wherein the semantic clothing attributes comprise at least one of:

a clothing category, a clothing part, or a clothing descriptor.

3. The machine-readable non-transitory storage medium of claim 1, wherein instructions to determine the probability of a semantic clothing attribute present in the images comprises instructions to:

create a semantic clothing attribute classifier for the clothing attribute based on a supervised learning method; and

determine the presence of the clothing attribute in the first and second images based on the semantic clothing attribute classifier.

4. The machine-readable non-transitory storage medium of claim 1, wherein determining a level of similarity between the first image and the second image comprises determining the similarity based on a comparison of a subset of the semantic clothing attributes.

5. The machine-readable non-transitory storage medium of claim 1, wherein determining a level of similarity between the first image and the second image comprises determining a similarity based on comparing the semantic clothing attributes in a particular order.

6. A computing system, comprising:

a storage to store information about image features associated with semantic clothing attributes;

a processor to;

determine a probability of a first semantic clothing attribute in a first image and a second image based on a comparison of the first image and the second image to the stored association information;

determine a probability of a second semantic clothing attribute in the first image and the second image based on a comparison of the first image and the second image to the stored association information;

determine a similarity level between the first image and the second image by comparing the probability of the semantic clothing attributes determined in the first image to the probability of the semantic clothing attributes determined in the second image; and

output information about the similarity between the first image and the second image.

7. The computing system of claim 6, wherein the processor is further to:

performing a supervised learning method on a training data set to determine the image features to associate with the semantic clothing attributes; and

store the information in the storage.

8. The computing system of claim 6, wherein determining a similarity level is further based on a comparison between the color of clothing in the first image and the second image.

9. The computing system of claim 6, wherein the processor is further to:

create a first vector indicating the probability of the first semantic clothing attribute and the probability of the second clothing feature in the first image;

create a second vector indicating the probability of the first semantic clothing attribute and the probability of the second semantic clothing attribute in the second image.

10. The computing system of claim 9, wherein determining the similarity level comprises determining the similarity level based on a comparison of the first vector and the second vector.

11. A method, comprising:

create a semantic clothing attribute classifier for associating an image feature with a semantic clothing attribute based on a supervised learning method performed on a training data set; and

determine a probability of the semantic clothing attribute in an image by comparing the image to the semantic clothing attribute classifier.

12. The method of claim 11, wherein the image features comprise at least one of:

a local shape of clothing, a local appearance of clothing, or a global appearance of clothing.

13. The method of claim 11, further comprising determining a level of similarity between the image and a second image based on the probability of the semantic clothing attribute in the image and the second image.

14. The method of claim 11, further comprising storing information in a data structure associated with the image indicating the probability of the semantic clothing attribute in the image.

15. The method of claim 11, wherein the semantic clothing attribute comprises at least one of:

a clothing category, a clothing part, or a clothing descriptor.