Browsing digital video

Li, Francis C.; Gupta, Anoop; Sanocki, Elizabeth; He, Long; Rui, Yong

doi:10.1145/332040.332425

Cited by 110 publications

(82 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although much work has been done on semantic browsers for edited videos, there is comparatively little work on unstructured videos, such as home videos or the casual capture of lectures, and even less on the automatic tagging of sections of unstructured videos [1]. VASTMM is a video browser designed to work with unstructured video [2].…”

Section: Video Browsersmentioning

confidence: 99%

See 1 more Smart Citation

Sort-Merge feature selection and fusion methods for classification of unstructured video

Morris

Kender

2009

2009 IEEE International Conference on Multimedia and Expo

View full text Add to dashboard Cite

We explore the problem of rapid automatic semantic tagging of video frames of unstructured (unedited) videos. We apply the Sort-Merge algorithm for feature selection on a large (>1000) heterogeneous feature set for videos showing lectures, to quickly locate low-level image features most predictive for concepts such as "key frame with text" or "key frame with computer source code". For evaluation, we introduce a "keeper" heuristic for feature retention, which provides a baseline comparison. We then compare early fusion and late fusion of diverse feature types; based on experiments on 12,395 frames, we find that in general late fusion offers higher Average Precision accuracy at lower computation cost, compared to early fusion. However, mergers of redundant feature types do not necessarily improve performance over single feature types; exploration of both merged and unmerged performance is necessary.

show abstract

Section: Video Browsersmentioning

confidence: 99%

“…We used 169 videos consisting of 172.8 hours. A segmentation algorithm developed for unstructured video as defined in [1], set to maximum granularity, generated 66130 virtual key frames. We employed random subsampling to speed the experiments, as in [5], and three-fold cross-validation for the SVM learning.…”

Section: Data Setmentioning

confidence: 99%

Sort-Merge feature selection and fusion methods for classification of unstructured video

Morris

Kender

2009

2009 IEEE International Conference on Multimedia and Expo

View full text Add to dashboard Cite

show abstract

“…For example, various layouts for video keyframes for gist understanding and for searching have been studied in [11,12,13,14,15]. Selecting the right keyframes for presenting the result of a user's video query has also been studied [16] and interactive playback tools that allow efficient content-browsing [17,18], interactive montages of map and timelines to visualise news video contents [19] have been evaluated. These experiments address some of the end-user's experiences with video retrieval systems, in other words, studying the constituent elements of a more complete, integrated, full multimedia retrieval system by attempting to explore and optimise the details of the best methods to use in these searching/browsing/presentation tools.…”

Section: Retrievalmentioning

confidence: 99%

Collaborative video searching on a tabletop

et al. 2006

View full text Add to dashboard Cite

Almost all system and application design for multimedia systems is based around a single user working in isolation to perform some task yet much of the work for which we use computers to help us, is based on working collaboratively with colleagues. Groupware systems do support user collaboration but typically this is supported through software and users still physically work in independently. Tabletop systems such as the DiamondTouch from MERL, are interface devices which support direct user collaboration on a tabletop. When a tabletop is used as the interface for a multimedia system, such as a video search system, then this kind of direct collaboration raises many questions for system design. In this paper we present a tabletop system for supporting a pair of users in a video search task and we evaluate the system not only in terms of search performance but also in terms of user-user interaction and how different user personalities within each a pair of searchers impacts search performance and user interaction. Incorporating the user into the system evaluation as we have done here reveals several interesting results and has important ramifications for the design of a multimedia search system.

show abstract

“…Video can be an audio-and video-centric genre (Li et al 2000). Automatic Speech Recognition (ASR) technology has been developed to turn audio into text (Christel et al, 1998) and to provide textual description of the video content.…”

Section: Related Researchmentioning

confidence: 99%

“…There is clearly interest in user access to digital video content. For example, Chen and Choi (2005) reported that more than 80% of participants in a study of anal og video usage by college students would be interested in accessing videos online if the y were available.Video can be an audio-and video-centric genre (Li et al 2000). Automatic Speech Recognition (ASR) technology has been developed to turn audio into text (Christel et al, 1998) and to provide textual description of the video content.…”

mentioning

confidence: 99%

Semantic visual features in content‐based video retrieval

2006

Proc of Assoc for Info

View full text Add to dashboard Cite

A new semantic visual features (e.g., car, mountain, and fire) navigation technology is proposed to improve the effectiveness of video retrieval. Traditional temporal neighbor browsing technology allows users to navigate temporal neighbors of a selected sample frame to find additional matches, while semantic visual feature browsing enables users to navigate keyframes that have similar features to the selected sample frame. A pilot evaluation was conducted to compare the effectiveness of three video retrieval designs that support 1) temporal neighbor browsing; 2) semantic visual feature browsing; and 3) fused browsing which is a combination of both temporal neighbor and semantic visual feature browsing. Two types of searching tasks: visual centric and non-visual centric tasks were applied. Initial results indicated that the semantic visual feature browsing system was more efficient for non-visual centric tasks. IntroductionAccess to digital video from news sources such as CNN, MSNBC, or ABC has become commonplace. To make digital multimedia resource discovery and search more convenient, multimedia digital libraries are being developed for research and education.Increasingly, students or instructors are consulting video col lections in search of video shots within larger video "documents" to be used in their projects or lectures. Viewing all videos in full length to find the desired video shots may be feasible for a small collection, but can be very time intensive for a large collection. The ability to search within individual videos, much in the same way that full text searching allows users to search for content instead of their bibliographic surrogates, would g reatly increase access to video content. Recent research on content-based video retrieval indicated that initially performing a text-based query and subsequently proceeding with neighbor or visual similarity browsing proved to be an effective retrieval strategy (Wildemuth et al., 2003;Heesch et al., 2004;Mezaris et al., 2004 ; Amir et al., 2005). Human beings are usually good at pattern recognition through navigation. A retrieval system supporting navigation functions would provide users additional means for content rel ated searching tasks.In this paper we propose a new video content browsing techniqu e: semantic visual feature browsing. Our purpose is to evaluate its effectiveness as compared to traditional temporal neighbor browsing technique for two types of retrieval tasks: visual centric tasks and non-visual centric tasks. After the introduction of related research, a description of the semantic visual feature browsing algorithm will be given. The user interface of a prototype web-based video retrieval system that supports semantic visual feature browsing will be then illustrated. Finally, the methodology of a pilot user study and some initial results from the study will be presented, fol lowed by a brief discussion. Related ResearchVideo retrieval in the context of a digital library has only recently begun to be studied from a research perspective...

show abstract

Browsing digital video

Cited by 110 publications

References 11 publications

Sort-Merge feature selection and fusion methods for classification of unstructured video

Sort-Merge feature selection and fusion methods for classification of unstructured video

Collaborative video searching on a tabletop

Semantic visual features in content‐based video retrieval

Contact Info

Product

Resources

About