ADAPTIVE EVENT TIMELINE IN CONSUMER IMAGE COLLECTIONS

Abstract

A method for organizing an event timeline for a digital image collection, includes using a processor for detecting events in the digital image collection and each event's associated timespan; determining the detected events that are significant in the digital image collection; and organizing the event timeline so that the event timeline shows the significant events and a clustered representation of the other events, made available to the user at different time granularities. The organized event timeline is also used for selecting images for generating output.

Description

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a Continuation of U.S. application Ser. No. 12/710,666, filed Feb. 23, 2010, incorporated herein by reference in its entirety.

FIELD

The disclosure relates generally to the field of digital image processing, and in particular to a method for organizing groups of digital images for efficient management and retrieval of events in consumer image collections.

BACKGROUND

The proliferation of digital cameras, camera phones, and scanners has lead to an explosion of digital images and videos, creating large personal multimedia databases. Since taking digital pictures is easy and practically free, consumers no longer restrict picture-taking to important events and special occasions. Images are being captured frequently, and of day-to-day occurrences in the consumers' life. Since a typical user has already accumulated many years of digital images and videos, browsing the collection to find images and videos taken during particular events is a very time-consuming process for the consumer.

There has been work in grouping images into events. U.S. Pat. No. 6,606,411 and U.S. Pat. No. 6,351,556 disclose algorithms for clustering image content by temporal events and sub-events. The above two patents teach how to cluster images and videos in a digital image collection into temporal events and sub-events. The terms “event” and “sub-event” are used in an objective sense to indicate the products of a computer mediated procedure that attempts to match a user's subjective perceptions of specific occurrences (corresponding to events) and divisions of those occurrences (corresponding to sub-events). Another method of automatically organizing images into events is disclosed in U.S. Pat. No. 6,915,011. The events detected are chronologically ordered in a timeline from earliest to latest.

Using the above methods, the amount of browsing required by the user to locate a particular event can be reduced by viewing representatives of the events along a timeline, instead of each image thumbnail. However, a typical user can still generate over 100 of such events for each year, and more prolific picture-takers can easily exceed a few hundred detected events per year. There is still a need to create an overall event timeline structure that is adaptable to the user's picture taking behavior as well as the time granularity of the events in their collections, and the ability to select a sub-set of events that summarizes the overall collection or a given time period in the collection.

SUMMARY

In accordance with the present disclosure, there is provided a method for organizing an event timeline for a digital image collection comprising, using a processor for:

(a) detecting events in the digital image collection and each event's associated timespan;

(b) determining the detected events that are significant in the digital image collection; and

(c) organizing the event timeline so that the detected events on the event timeline between the detected significant events are grouped into clusters on the event timeline.

Since the number of images and videos in a typical user's collection is growing rapidly and each user might have a different picture-taking behavior, there is a critical need to provide the user efficient access and retrieval of images and important events from their collections. In this disclosure, a user's picture-taking behavior in terms of detected events is translated into a timeline, where there is a data point for each time step. Then an adaptive event-based timeline is created where both the significance of the detected events as well as the time granularity of the events are taken into account.

The organization and retrieval of images and videos is a problem for the typical consumer. It is useful for a user to be able to easily access and browse an overview of events in their image collection. Technology disclosed in prior art permits the classification of images in a collection into events, but not the ability to adaptively create an event timeline that can be tailored to individual users according to their picture-taking behaviors. As a result, there is a lack of effective mechanisms for finding and accessing events that are of importance to the users. This disclosure creates an effective event-based timeline at different time scales, where both the significance of the detected events as well as the time granularity of the events is taken into account. This organized event timeline can be used for selecting images and determining emphasis when creating outputs such as albums and slideshows from a collection of images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system that practices the present disclosure;

FIG. 2 is an overall flowchart of the method practiced by FIG. 1;

FIG. 3 is a more detailed flowchart showing one embodiment of the method of FIG. 2;

FIG. 4 is a more detailed flowchart of a second embodiment of the method of FIG. 2;

FIG. 5 shows a specific example of the input and output of the event timeline organizer 120 of FIG. 2;

FIG. 6 shows a specific example of events generated at different granularity settings 215;

FIG. 7 is a specific example of the input and output of the event timeline organizer 320 of FIG. 4; and

FIG. 8 is a flowchart of the use of the organized event timeline for output creation.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure can be implemented in computer systems as will be well known to those skilled in the art. In the following description, some embodiments of the present disclosure will be described as software programs. Those skilled in the art will readily recognize that the equivalent of such a method can also be constructed as hardware or software within the scope of the disclosure.

Because image manipulation algorithms and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, the method in accordance with the present disclosure. Other aspects of such algorithms and systems, and hardware or software for producing and otherwise processing the image signals involved therewith, not specifically shown or described herein can be selected from such systems, algorithms, components, and elements known in the art. Given the description as set forth in the following specification, all software implementation thereof is conventional and within the ordinary skill in such arts. Videos in a collection are included in the term “images” in the rest of the description.

The present disclosure can be implemented in computer hardware and computerized equipment. For example, the method can be performed in a digital camera, a multimedia smart phone, a digital printer, on an internet server, on a kiosk, and on a personal computer. Referring to FIG. 1, there is illustrated a computer system for implementing the present disclosure. Although the computer system is shown for the purpose of illustrating a preferred embodiment, the present disclosure is not limited to the computer system shown, but can be used on any electronic processing system such as found in digital cameras, home computers, kiosks, or any other system for the processing of digital images. A computer 10 includes a microprocessor-based unit 20 (also referred to herein as a processor) for receiving and processing software programs and for performing other processing functions. A memory unit 30 stores user-supplied and computer-generated data which can be accessed by the processor 20 when running a computer program. A display device (such as a monitor) 70 is electrically connected to the computer 10 for displaying information and data associated with the software, e.g., by use of a graphical user interface. A keyboard 60 is also connected to the computer. As an alternative to using the keyboard 60 for input, a mouse can be used for moving a selector on the display device 70 and for selecting an item on which the selector overlays, as is well known in the art. Input devices 50 such as compact disks (CD) and DVDs can be inserted into the computer 10 for inputting the software programs and other information to the computer 10 and the processor 20. Still further, the computer 10 can be programmed, as is well known in the art, for storing the software program internally. In addition, media files (such as images, music and videos) can be transferred to the memory 30 of the computer 10 by use of input devices 50 such as memory cards, thumb drives, CDs and DVDs, or by connecting a capture device (such as camera, cell phone, video recorder) directly to the computer 10 as an input device. The computer 10 can have a network connection 80, such as a telephone line or wireless connection, to an external network, such as a local area network or the Internet. Software programs and media files can be transferred to the computer 10 from other computers or the Internet through the network connection 80.

It should also be noted that the present disclosure can be implemented in a combination of software or hardware and is not limited to devices which are physically connected or located within the same physical location. One or more of the devices illustrated in FIG. 1 can be located remotely and can be connected via a network. One or more of the devices can be connected wirelessly, such as by a radio-frequency link, either directly or via a network.

Referring to FIG. 2, a user's digital image collection 105 resides in the memory 30 of the computer 10. The other blocks in the figure are implemented by a software program and are executed by the processor 20 of the computer 10. The digital image collection 105 is provided to an event detector 110 and a significant event detector 115. The event detector 115 groups a user's digital image collection 105 into an event representation. In the preferred embodiment, the digital image collection 105 is grouped into temporal events and sub-events using event and sub-event detectors described in U.S. Pat. No. 6,606,411 and U.S. Pat. No. 6,351,556. The above two patents teach how to cluster images and videos in a digital image collection into temporal events and sub-events. The terms “event” and “sub-event” are used in an objective sense to indicate the products of a computer mediated procedure that attempts to match a user's subjective perceptions of specific occurrences (corresponding to events) and divisions of those occurrences (corresponding to sub-events). Briefly summarized, a collection of images is classified into events by determining a threshold for time differences between adjacent images of the collection based on a two-mean clustering of a histogram of time differences between adjacent images over the entire collection. The images are separated into groups corresponding to events based on having time differences at the boundary that are higher than the threshold determined in the earlier step. For each event, sub-events can be determined (if any) by comparing the color histogram information of successive images as described in U.S. Pat. No. 6,351,556. This is accomplished by dividing an image into a number of blocks and then computing the color histogram for each of the blocks. A block-based histogram correlation procedure is used as described in U.S. Pat. No. 6,351,556 to detect sub-event boundaries. Another method of automatically organizing images into events is disclosed in U.S. Pat. No. 6,915,011. Briefly summarized, according to one aspect of the above disclosure, an event clustering method uses foreground and background segmentation for clustering images from a group into similar events. Initially, each image is divided into a plurality of blocks, thereby providing block-based images. Using a block-by-block comparison, each block-based image is segmented into a plurality of regions including at least a foreground and a background. One or more luminosity, color, position or size features are extracted from the regions and the extracted features are used to estimate and compare the similarity of the regions including the foreground and background in successive images in the group. Then, a measure of the total similarity between successive images is computed, thereby providing image distance between successive images, and event groups are delimited from the image distances. The event timespan is the time between the beginning and end of the event, where the beginning of the event is represented by the capture time of the first image in the event, and the end is represented by the capture time of the last image of the event (or the end of capture of the video segment if the event ends with a video clip).

Referring to FIG. 2, the significant event detector 115 identifies significant events in the digital image collection 105. The digital image collection 105 is provided to the significant event detector 115. The detector uses the image capture date and time information extracted from the EXIF metadata stored in the image files of the digital image collection 105 by the capture device (such as a camera) to generate a time-series that shows the number of images captured on each calendar day of the time period covered by the digital image collection 105. This time-series is analyzed to find a model that fits the series. There are many well established methods for time-series modeling (“Introduction to Time Series and Forecasting”, Brockwell and Davis, Springer-Verlag 2002). The model that is appropriate in this situation is the ARIMA (Auto-Regressive Integrated Moving Average) model (Brockwell and Davis, supra, pp 179-187). The model ARIMA (p,d,q) has three main parameters—p being the order of the autoregressive component, q being the order of the moving average component and d being the order of differencing required for dealing with the deviations from stationarity. Significant events in the collection are identified as occurring on the calendar days where the residuals between the fitted model and the actual time-series are higher than a specified threshold i.e. where the fitted model is less able to predict the actual data.

Referring to FIG. 2, an event timeline organizer 120 accepts as input the events detected by the digital image collection 105 and the significant events identified by the significant event detector 115. In one embodiment, the event timeline organizer 120 retains the significant events on the timeline, and groups the events located between adjacent significant events on the timeline into clusters called meta-events. Each event occupies a section of the timeline specified by its timespan. As an example, the top row of FIG. 5 shows a timeline with events and significant events marked on it. The event timeline organizer produces the output shown in the bottom row of FIG. 5, where events between the significant events have been clustered into meta-events. A resulting organized event timeline 150 has a reduced number of top-level groupings that include meta-events (that are clusters of events) and significant events on the timeline. The user is therefore, presented with a less cluttered top-level view, while still keeping potential events of interest (the significant events) accessible at the top-level view. Other events can be accessed by expanding the meta-events, which can be described by the time durations they cover (for example, “Feb. 20-Apr. 2, 2009”).

The method of U.S. Pat. No. 6,606,411 also provides a few key parameters for adjusting the number of events and sub-events generated from any given collection of images. The main parameter, “granularity”, determines the overall granularity of event clustering. Valid values are in the range [−1, 1] with 0 being the “optimal” tuned setting. Larger values increase the number of events found by the algorithm while smaller values decrease the number of events. The actual internal mechanism has to do with adjusting the various thresholds for the two-means clustering. Some of these thresholds include high and low thresholds for global histogram comparisons and high and low thresholds for multi-block histogram comparisons to determine whether to merge two events or two sub-events. The events detected continue to be chronologically ordered in a timeline from earliest to latest. FIG. 6 is an illustration of the set of events generated using different settings of the granularity parameter. The graph plots the number of events detected during the span of one year. In the example shown, the final number of events for the entire year varies from less than 10 to close to 170 based on the granularity parameter setting provided.

Referring to FIG. 3, the digital image collection 105 is processed by event detectors 215 with the granularity parameter set at a range of values covering the [−1, 1] range. The digital image collection 105 is also processed by the significant event detector 115 and the output is provided to an event timeline organizer 320. In this embodiment of the event timeline organizer 320, the timeline is broken up into time periods around significant events. The boundaries of the time periods are at the mid-point of the time between any two adjacent significant events. In each time period containing a significant event, the events detected at the smallest granularity setting (producing fewest events) is selected that still contains the significant event as a detected event. For example, referring to FIG. 7, the top three timelines show the significant event detector 115 output at different granularities (granularity 1 being the smallest, and granularity 3 being the largest). The significant events are the highlighted events. The last row shows an organized event timeline 350 with different time periods of the time-line selecting events from different granularity settings, based on the smallest granularity that provides a correct representation of the significant event in that time period. This method ensures that significant events are available on the organized timeline 350 and other events are presented at appropriate granularity around the significant events.

Referring to FIG. 4, the digital image collection 105 is processed by event detectors 215 with the granularity parameter set at a range of values covering the [−1, 1] range. The digital image collection 105 is also processed by the significant event detector 115 using time bins of different granularities. A set of time units are determined—these time units can be a few months (capturing a season), a month, a week, a day, or hours—the size of the unit being referred to as granularity. The time-series at different granularities is produced by counting the number of images captured in the given time unit or bin. The output of the event detectors 215 and the significant event detector 115 at different granularities 415 is provided to a time granularity selector 425, along with additional inputs 420. The time granularity selector 425 selects a time granularity and extracts the set of events and significant events corresponding to that time granularity from its input sets. The additional inputs 420 can include user actions, system requirements or user preferences. The set of events and the significant events selected are passed on to the event timeline organizer 320 described earlier to produce an organized timeline 450. The event timeline organizer 120 can also be used instead of event timeline organizer 320 to produce a different organized timeline for each time granularity. In one embodiment of the disclosure, the user can then select the timeline from this set of event timelines. The additional inputs 420 are used as follows. In a browsing application, events and significant events can be selected at the time granularity at which the user selects to view the collection. For example, if the user is viewing a short time-span of a few weeks, a finer granularity setting (higher number) is used for selecting the events and significant events; whereas, if the user is viewing a longer time-span (e.g. the collection over five years), a lower value of granularity is selected for events and significant events. The system requirement in terms of display capability can also dictate the number of top-level events on the organized event timeline, and therefore, the granularity selected. For example, if approximately 10 events will fit the display, then the granularity is selected so that the number of events and significant events is close to that number. The user can also set the preference for viewing the events at a certain granularity, and the time granularity selector selects this setting for the given user.

One of the challenges of creating an output album (hard copy) or slideshow (soft copy) from a collection of images is the selection of images to include and the emphasis to be placed on each of the selected images. Referring to FIG. 8, the organized event timeline 810 (which can be 150, 350 or 450) is provided to a media selection module 820 which selects the images to be used from the digital image collection 105. The media selection module 820 also receives the output specifications 815 as input. The output specifications 815 include the number of images to be selected, and the section of the timeline to be used for the selection e.g. select 12 images from a one year time-span to generate a picture calendar as output. The media selection module 820 prioritizes events in the time-span selected as follows. The events are weighted in proportion to the number of images in the event, after inflating the number of images in significant events by multiplying by a significance factor (e.g. 2.0). The number of images in the output specification is distributed among the events proportional to their weight. Let . . . be the image counts of m events, where

n=(significance factor*actual image count) for significant events
and n=actual image count for the rest of the (non-significant) events. Then the weight assigned to any event k is

$\frac{n_k}{\sum _{i=1}^mn_i},$

and the number of images assigned to this event is the (weight*number of images specified by the output specifications). Note that fractional number of images assigned will need to be rounded to whole numbers, and therefore, some events can receive 0 images assigned to them (if they are assigned less than 0.5 images).

The selection of a given number of images from a specific event can be done simply based on sampling the event images at equal intervals. There are also more intelligent methods for performing this selection—taking into account image quality, diversity and specific semantic features such as people and objects present—which can be used in this step. For example, the selection can be based on the technical quality of the image (A. Loui, M. Wood, A. Scalise, and J. Birkelund, “Multidimensional image value assessment and rating for automated albuming and retrieval,” Proc. IEEE Intern. Conf. on Image Processing (ICIP), San Diego, Calif., Oct. 12-15, 2008) or aesthetic quality of an image (C. Cerosaletti, and A. Loui, “Measuring the perceived aesthetic quality of photographic images,” Proc. 1st International Workshop on Quality of Multimedia Experience (QoMEX), San Diego, Calif., Jul. 29-31, 2009). However, this is not a focus of the present disclosure which aims to provide a selection and weighting mechanism at the event-level.

The selected images are provided to an emphasis operator 825. The emphasis operator 825 generates emphasis scores to highlight particular images from the selected set. In one embodiment, images from non-significant events have the default emphasis score, whereas the selected images that belong to significant events are assigned emphasis scores by multiplying the default score by a significance factor greater than 1.0. The emphasis scores can be based on the score for the technical quality of the image (: A. Loui, M. Wood, A. Scalise, and J. Birkelund, “Multidimensional image value assessment and rating for automated albuming and retrieval,” Proc. IEEE Intern. Conf. on Image Processing (ICIP), San Diego, Calif., Oct. 12-15, 2008) or aesthetic quality of an image (C. Cerosaletti, and A. Loui, “Measuring the perceived aesthetic quality of photographic images,” Proc. 1st International Workshop on Quality of Multimedia Experience (QoMEX), San Diego, Calif., Jul. 29-31, 2009), or a combination of the two scores. The output creation module 850 uses the emphasis score and image selection to produce a creative presentation of the images. For hard copy (such as photo-books, calendars, cards), the emphasis score can control the size of the image in the output or the prominence of position in the page. For soft copy (such as a slideshow on digital photo frames, computer monitor or other display devices), the emphasis score can be used to assign screen time for the image i.e. images with higher emphasis scores will be displayed for longer duration. Higher emphasis scores can also provide triggers for highlighting such as decorative boxes around the image in both forms of presentation.

This disclosure has been described in detail with particular reference to certain preferred embodiments thereof, but it will be understood that variations and modifications can be effected within the spirit and scope of the disclosure.

PARTS LIST

• 10 Computer
• 20 Processor
• 30 Memory
• 50 Input device
• 60 Keyboard
• 70 Display device
• 80 Network connection
• 105 Digital image collection
• 110 Event detector
• 115 Significant event detector
• 120 Event timeline organizer
• 150 Organized event timeline
• 215 Event detectors at different granularities
• 320 Event timeline organizer
• 350 Organized event timeline
• 415 Significant event detector at different granularities
• 420 Additional inputs
• 425 Time granularity selector
• 450 Organized event timeline
• 810 Organized event timeline
• 815 Output specifications
• 820 Media selection module
• 825 Emphasis operator
• 850 Output creation module

Claims

1. A method comprising:

detecting, by a computing device, a plurality of events in a plurality of digital images and an associated timespan for each event of the plurality of events;

generating a time series that indicates a number of images captured during a time period covered by the digital image collection;

identifying a set of events by analyzing the time series using time-series modeling; and

organizing, by the computing device, the plurality of events into an event timeline, such that detected events on an event timeline between the determined set of events are grouped into clusters on the event timeline.

2. The method of claim 1, wherein the detecting the plurality of events comprises detecting the plurality of events based on a selected time granularity.

3. The method of claim 2, wherein selecting the granularity of event detection is based on choosing the fewest detected events that preserve the significant events during a given time period.

4. The method of claim 1, further comprising enabling user browsing of images associated with at least one detected event of the plurality of events.

5. The method of claim 1, further comprising:

presenting a display of the event timeline; and

receiving a user selection of the event timeline.

6. The method of claim 4, further comprising enabling user browsing of images associated with at least one detected event of the plurality of events for the selected first event timeline.

7. The method of claim 1, further comprising:

producing an emphasis score using an emphasis operator; and

controlling a presentation of an output of images based on the emphasis score.

8. The method of claim 1, further comprising analyzing the time series to find a model that identifies the significant events.

9. The method of claim 8, wherein the identifying comprises determining when the model is less able to predict actual data of the time series.

10. A system comprising:

a memory; and

a processor coupled to the memory, wherein the processor is configured to: detect a plurality of events in a plurality of digital images and an associated timespan for each event of the plurality of events; generate a time series that indicates a number of images captured during a time period covered by the digital image collection; identify a set of events by analyzing the time series using time-series modeling; and organize the plurality of events into an event timeline, such that detected events on an event timeline between the determined set of events are grouped into clusters on the event timeline.

11. The system of claim 10, wherein the detection of the plurality of events comprises a detection of the plurality of events based on a selected time granularity.

12. The system of claim 11, wherein the selection of the granularity of event detection is based on choosing the fewest detected events that preserve the significant events during a given time period.

13. The system of claim 10, wherein the processor is further configured to enable user browsing of images associated with at least one detected event of the plurality of events.

14. The system of claim 10, wherein the processor is further configured to:

present a display of the event timeline; and

receive a user selection of the event timeline.

15. The system of claim 10, wherein the processor is further configured to:

produce an emphasis score using an emphasis operator; and

control a presentation of an output of images based on the emphasis score.

16. A non-transitory computer-readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations comprising:

detecting a plurality of events in a plurality of digital images and an associated timespan for each event of the plurality of events;

generating a time series that indicates a number of images captured during a time period covered by the digital image collection;

identifying a set of events by analyzing the time series using time-series modeling; and

organizing the plurality of events into an event timeline, such that detected events on an event timeline between the determined set of events are grouped into clusters on the event timeline.

17. The computer-readable medium of claim 16, wherein the detecting the plurality of events comprises detecting the plurality of events based on a selected time granularity.

18. The computer-readable medium of claim 17, wherein selecting the granularity of event detection is based on choosing the fewest detected events that preserve the significant events during a given time period.

19. The computer-readable medium of claim 16, further comprising enabling user browsing of images associated with at least one detected event of the plurality of events.

20. The computer-readable medium of claim 16, further comprising:

presenting a display of the event timeline; and

receiving a user selection of the event timeline.