Method for automatically classifying images into events

• US 6,606,411 B1
• Filed: 09/30/1998
• Issued: 08/12/2003
• Est. Priority Date: 09/30/1998
• Status: Expired due to Term

• Current Assignee: Monument Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: Eastman Kodak Company
• Inventors: Pavie, Eric S., Loui, Alexander C.
• Primary Examiner(s): Mehta, Bhavesh M.
• Assistant Examiner(s): AZARIAN, SEYED H
• Application Number:
  US09/163,618
• Time in Patent Office: 1,777 Days
• Field of Search: 382/132, 382/103, 382/173, 382/177, 382/229, 382/203, 382/224, 382/260, 382/128, 382/164, 382/168, 382/162, 382/170, 382/165, 382/225, 382/305, 348/169, 348/699, 358/400, 358/402, 706/52, 395/140
• US Class Current: 382/224
• 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 18/23 Clustering techniques
• CPC Class Codes: G06V 10/507 Summing image-intensity values; Histogram projection analysis
• CPC Class Codes: G06V 10/758 Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 10/762 using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 20/00 Scenes; Scene-specific elements control of digital cameras H04N23/60
• CPC Class Codes: G06V 2201/10 Recognition assisted with metadata

Abstract

A method for automatically classifying images into events, the method includes the steps of: receiving a plurality of images having either or both date and/or time of image capture; determining one or more largest time differences of the plurality of images based on clustering of the images; and separating the plurality of images into the events based on having one or more boundaries between events which one or more boundaries correspond to the one or more largest time differences.

Claims

1. A method for automatically classifying images into events, the method comprising the steps of:

(a) receiving a plurality of images having either or both date and/or time of image capture;

(b) determining one or more largest time differences of the plurality of images based on time and/or date clustering of the images; and

,

(c) separating the plurality of images into the events based on having one or more boundaries between events which one or more boundaries correspond to the one or more largest time differences.

2. The method as in claim 1, wherein step (b) includes computing a time difference histogram and performing a 2-means clustering on the time difference histogram for defining the one or more boundaries.

3. The method as in claim 2, wherein step (b) further includes mapping the time difference histogram through a time difference scaling function before performing the 2-means clustering.

4. The method as in claim 2, wherein step (c) includes checking the images adjacent the one or more boundaries for similarity by comparing content of the images.

5. The method as in claim 4, wherein step (c) includes checking the images adjacent the one or more boundaries for similarity by using a block-based histogram correlation technique.

6. The method as in claim 5 further comprising step (d) dividing the events into subject grouping by using an image content analysis.

7. The method as in claim 6, wherein step (d) includes dividing the events into subject grouping by using a block-based histogram technique.

8. A method for automatically classifying images into events, the method comprising the steps of:

(a) receiving a plurality of images having either or both date and/or time of image capture;

(b) determining one or more largest time differences of the plurality of images based on time and/or date clustering of the images;

(c) separating the plurality of images into the events based on having one or more boundaries between events which one or more boundaries correspond to the one or more largest time differences; and

(d) analyzing the events for content by dividing the images into a plurality of blocks and grouping the images into subject grouping based on block-based histogram correlation which includes computing a color histogram of each block and computing a histogram intersection value which determines the similarity between blocks, thereby refining and improving the overall classification and subject grouping of the events.