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Kazutaka Hirata

dblp:35/3356 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
User interface design and tools › visual programming
visual query formulation
0.012004
A visuospatial memory cue system for meeting video retrieval · ACM Multimedia 2004

Methods — techniques the papers use, named apart from their topics

memory-cue paradigm · 0.1
YearPublicationVenuePosition
2004 A visuospatial memory cue system for meeting video retrieval
abstract
We present a system based on a new, memory-cue paradigm for retrieving meeting video scenes. The system graically represents important memory retrieval cues such as room layout, participant's faces and sitting positions, etc.. Queries are formulated dynamically: as the user graically manipulates the cues, the query results are shown. Our system (1) helps users easily express the cues they recall about a particular meeting; (2) helps users remember new cues for meeting video retrieval. We discuss the experiments that motivate this new approach, implementation, and future work.
Takeshi Nagamine, Alejandro Jaimes, Kengo Omura, Kazutaka Hirata
ACM Multimedia4
2004 Interactive visualization of multi-stream meeting videos based on automatic visual content analysis
abstract
We present a new approach to segment and visualize informally captured multi-stream meeting videos. We process the visual content in each stream individually by analyzing the differences between frames in each sequence to find change areas. These results are combined with face detection to determine visual activity in each of the streams. We then combine the activity scores from multiple streams and automatically generate a 3D representation of the video. Our representation allows the user to obtain an at-a-glance view of the video at different granularities of activity, view multiple streams simultaneously, and select particular points in time for viewing. We present experiments that suggest that low-level visual analysis can be effective for finding highlights that can be used for browsing multi-stream meeting videos.
Alejandro Jaimes, Naofumi Yoshida, Kazumasa Murai, Kazutaka Hirata, Jun Miyazaki
MMSP4