Image retrieval systems and methods with semantic and feature based relevance feedback

• US 20050055344A1
• Filed: 10/20/2004
• Published: 03/10/2005
• Est. Priority Date: 10/30/2000
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Lu, Ye, Zhang, Hong-Jiang, Liu, Wen-Yin
• Granted Patent: US 7,499,916 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/58 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 16/5854 using shape and object relationship
• CPC Class Codes: G06F 16/5862 using texture
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

An image retrieval system performs both keyword-based and content-based image retrieval. A user interface allows a user to specify queries using a combination of keywords and examples images. Depending on the input query, the image retrieval system finds images with keywords that match the keywords in the query and/or images with similar low-level features, such as color, texture, and shape. The system ranks the images and returns them to the user. The user interface allows the user to identify images that are more relevant to the query, as well as images that are less or not relevant to the query. The user may alternatively elect to refine the search by selecting one example image from the result set and submitting its low-level features in a new query. The image retrieval system monitors the user feedback and uses it to refine any search efforts and to train itself for future search queries. In the described implementation, the image retrieval system seamlessly integrates feature-based relevance feedback and semantic-based relevance feedback.

Claims

1. A computer-readable medium having computer-executable instructions for performing steps comprising:

initiating a search for images based on at least one query keyword in a query;

identifying, during the search, first images having associated keywords that match the query keyword, and second images that contain low-level features similar to those of the first images identified as matching the query keyword, wherein the low-levels features comprise color, shape, and texture and the low-level features do not match the search query;

ranking the first and second images;

presenting the first and second images to a user; and

receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

assigning a large weight to an association between the query keyword and the images deemed relevant by the user and/or assigning a small weight to an association between the query keyword and the example image.

2. The computer readable medium of claim 1 having further computer-readable instructions for performing the step of presenting the first and second images.

5. The computer readable medium of claim 1 wherein the computer-readable instructions for performing the step of monitoring includes computer-readable instructions for performing steps comprising

presenting the first and second images to a user;

receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

assigning a large weight to an association between the query keyword and the images deemed relevant by the user.

6. The computer readable medium of claim 5 having further computer-readable instructions for performing the step of grouping the low-level features of the images deemed relevant by the user.

7. The computer readable medium of claim 1 wherein the computer-readable instructions for performing the step of monitoring includes computer-readable instructions for performing steps comprising

presenting the first and second images to a user;

receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

assigning a small weight to an association between the query keyword and the example image.

8. The computer readable medium of claim 7 having further computer-readable instructions for performing the step of identifying additional images with low-level features similar to those of the example image.

3. A computer-readable medium having computer-executable instructions for performing steps comprising:

initiating a search for images based on at least one query keyword in a query;

identifying, during the search, first images having associated keywords that match the query keyword, and second images that contain low-level features similar to those of the first images identified as matching the query keyword wherein the low-levels features comprise color, shape, and size and the low-level features do not match the search query;

presenting the first and second images to a user;

receiving feedback from the user as to whether the first and second images are relevant to the query;

learning how the first and second images are identified based on the feedback from the user; and

receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

assigning a large weight to an association between the query keyword and the images deemed relevant by the user and/or assigning a small weight to an association between the query keyword and the example image.

4. The computer readable medium of claim 3 wherein the computer-readable instructions for performing the step of monitoring includes computer-readable instructions for performing steps comprising

presenting the first and second images to a user;

receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

refining the search to identify additional images that contain low-level features similar to those of the images indicated by the user as being relevant to the query.

9. A computer-readable medium having computer-executable instructions for performing steps comprising:

permitting entry of both keyword-based queries and content-based queries;

finding a first set of images using semantic-based image retrieval and a second set of images using low-level feature-based image retrieval based on the first set of images wherein the low-levels features comprise color, shape and texture and the low-level features do not match the semantic-based and the low-level features do not match the keyword-based or content-based;

presenting the images to a user so that the user can indicate whether the images are relevant;

conducting semantic-based relevance feedback and low-level feature-based relevance feedback in an integrated fashion; and

receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

assigning a large weight to an association between the query keyword and the images deemed relevant by the user and/or assigning a small weight to an association between the query keyword and the example image.

10. The computer readable medium of claim 9 having further computer-readable instructions for performing the step of ranking the images.

11. The computer readable medium of claim 9 having further computer-readable instructions for performing the step of using images indicated as being relevant to find additional images.

12. A computer-readable medium having computer-executable instructions for performing steps comprising:

presenting a result set of images that are returned from an image retrieval search of a query having at least one keyword;

identifying the set of images from the presenting retrieved using the at least one keyword and second images that contain low-level features similar to those of the first images identified as matching the at least one keyword, wherein the low-levels features comprise color, shape and texture and the low-level features do not match the search query;

monitoring feedback from a user as to whether the images in the result set are relevant to the query;

in an event that the user selects at least one image as being relevant to the query, associating the keyword in the query with the selected image to form a first keyword-image association and assigning a comparatively large weight to the first keyword-image association; and

in an event that the user identifies an example image for refinement of the search, associating the keyword in the query with the example image to form a second keyword-image association and assigning a comparatively small weight to the second keyword-image association.

13. The computer readable medium of claim 12 having further computer-readable instructions for performing the step of conducting both content-based image retrieval and semantic-based image retrieval.

14. The computer readable medium of claim 12 having further computer-readable instructions for performing the step of presenting the result set of images in a user interface, the user interface facilitating the user feedback by allowing the user to indicate which images are more relevant and which images are less relevant.

15. An image retrieval system comprising:

a means for handling both keyword-based queries having one or more search keywords and content-based queries having one or more low-level features of an image; and

a means for identifying at least one of (1) first images having keywords that match the search keywords from a keyword-based query, and (2) second images having low-level features similar to the low-level features of a content-based query based on the first images, wherein the feature and semantic matcher ranks the images, and the low-level features comprise color, shape and texture, and the low-level features do not match the keyword-based or content-based;

a means for presenting the first and second images to a user; and

a means for receiving feedback from the user as to whether the first and second images are relevant to the query further comprising;

assigning a large weight to an association between the query keyword and the images deemed relevant by the user and/or assigning a small weight to an association between the query keyword and the example image.

16. An image retrieval system as recited in claim 15, wherein the means for handling comprises a natural language parser.

17. An image retrieval system as recited in claim 15, wherein the means for handling query handler comprises:

a parser to parse text-based queries; and

a concept hierarchy to define various categories of images.

18. An image retrieval system as recited in claim 15, further comprising

a means fir to presenting the images to a user identified by the feature and semantic matcher.

19. An image retrieval system comprising:

a means for handling both keyword-based queries having one or more search keywords and content-based queries having one or more low-level features of an image;

a means for identifying at least one of (1) first images having keywords that match the search keywords from a keyword-based query, and (2) second images having low-level features similar to the low-level features of a content-based query based on the first images, wherein the low-level features comprise color, shape and texture, and the low-level features do not match the keyword-based or the content-based;

a means for presenting to a user the images identified by the feature and semantic matcher to a user, the user interface allowing the user to indicate whether the images are relevant to the query; and

a means for training the image retrieval system based on user feedback as to relevancy, wherein the feedback analyzer to assign a large weight to an association between the query keyword and the images deemed relevant by the user and/or feedback analyzer to assign a small weight to an association between the query keyword and the example image and/or a feedback analyzer to assign a small weight to an association between the search keywords and the example image.

20. An image retrieval system as recited in claim 19, further comprising:

a means for presenting to the user the images identified by the feature and semantic matcher to a user, the user interface allowing the user to identify an example image; and

the means for identifying is configured to identify additional images that contain low-level features similar to those of the example image.

21. An image retrieval system as recited in claim 19, further comprising:

a means for presenting to the user the images identified by the feature and semantic matcher to a user, the user interface allowing the user to identify which images are relevant to a particular search query; and

a means for assigning a large weight to an association between the search keywords and the images identified as relevant by the user.

22. An image retrieval system as recited in claim 21, wherein the means for assigning groups the low-level features of the images identified as relevant by the user.

23. An image retrieval system as recited in claim 22, wherein the means for identifying identifies additional images with low-level features similar to those of the example image.