Why did you record this video?

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Why did you record this video?

An exploratory study on user intentions for video

production.

Mathias Lux* & Jochen Huber§

* Klagenfurt University, AT§ Technische Universität Darmstadt, DE

Motivation

• Novelty of intentions in MMIS

– have not (yet) been investigated thoroughly

• Hard, interdisciplinary problem

– fuzzy, social, deals with people

• Intentions are diverse

– have potential for distinguishing between different user groups

(cc) by bitzcelt, http://www.flickr.com/photos/bitzcelt/

Goals

• Find out if there is a taxonomy that can

be used for MMIS.

• Support or reject current approaches.

• Find path towards a usable model

supported by statistics.

(cc) by jam343 http://www.flickr.com/photos/jam343

Roots

• A taxonomy of web search (2002), A. Broder

– navigational – “dublin wikipedia”

– informational – “day tour dublin”

– transactional – “book hotel in dublin”

• Understanding user goals in web search

(2004), D. Levinson & D. Rose

– transactional -> resource

– more fine-grained sub categories

– informational > 60%

Roots

• A classification scheme for user

intentions in image search (2010), M.

Lux, C. Kofler, O. Marques

– 4th category: mental image

– categories overlap

Photo production

• The ubiquitous camera: An in-depth

study of camera phone use (2005), T.

Kindberg et al.

– Affection vs. function

– Social vs. individual

Video production

• Video microblogging: your 12 seconds of

fame (2010) N. Bornoe & L. Barkhuus

– social collaboration (not individual)

– self expression, entertainment, self representation

• Practices in creating videos with mobile

phones (2009), A. Puikkonen et al.

– preserve moment of interest

– sharing ”occasionally”, not by default

Methodology

• Exploratory study

– 20 participants (16m, 4f)

– semi-structured interviews

• Interviews

– demographics & general usage

– communication & recording habits

• Instances

– 48 situations were reported

Research Questions

Using Kindberg’s taxonomy as a basis

• Are Kindberg’s classes disjoint?

– are there instances that indicate overlap?

• Is a 2D space sufficient to describe video

production intentions

– need for other dimensions?

(cc) by oberazzi, http://www.flickr.com/photos/oberazzi/

Analysis

• Clustering of instances

– similar instances go together

– grouped manually

– discussed grouping

– multiple assignments possible

(cc) by alastanton, http://www.flickr.com/photos/alanstanton

Clustering

• Preservation

– Storing a scene to view it later

• Sharing

– Showing scenes to others

• Affection

– Capturing a scene due to emotion

• Functional

– Video is part of a job, hobby, etc.

• Technical interest

– E.g. trying out a camera

• Other

– Unknown or unmentioned intentions, etc.

Results

• Nearly all of the videos (39 /48) were

taken for sharing them.

– 29 of the 39 instances: family, friends, colleagues, other closed groups.

• Affection - 23 instances

• Preservation - 19 instances

Results

• Do class assignments co-occur?

• Cross-tabulation

– phi can be read like a correlation coefficient

– -1 <= min <= phi <= max <= 1

– min, max due to different number ofassignments

Discussion

• Multiple assignments

– 81% were assigned to more than 1 cluster

– Are classes disjoint (e.g. function vs. affection)?

P4 mentioned a video he took on a mountain while

snowboarding. He recorded the video because he “took

it because [he loves] snowboard video tricks and [he

thinks] that it is very important to reconsider them to

improve [his own] technique”.

Discussion

P10 reported “First my friend is so good at

singing and also charming and second he was

about to leave the city and that was our last

meeting. So I took the video to remember the

night”

• Ad-hoc affection vs.

• Preservation

Discussion

• Preservation opposite of sharing?

– No correlation in our data (A)

• Function & preservation go together?

– maximum neg. correlation (B)B

A

Conclusion

• Intention classes not disjoint in the

domain of video production

• Kindberg’s taxonomy is not sufficient for

video production

• Preservation, sharing, affection &

function are 4 valid classes to start with.

Future work

• Our proposed structure is biased by

– the small data set

– the convenience sample

– the questions asked

• Collected a data set for photos

– 1,309 photos + intentions of their photographers

– mturk validation and QA of the survey results

• Collection of a video data set

• Application in domains

(cc) by thevince, http://www.flickr.com/photos/thevince

Thanks …

.. for your interest

more on user intentions:

http://tinyurl.com/mlux-itec

check out LIRe CBIR library:

http://www.semanticmetadata.net

mlux@itec.uni-klu.ac.at

(cc) http://www.jumpingbrain.org/