Combining content-based analysis and crowdsourcing to improve user interaction with zoomable video

Carlier, Aurélie; Ravindra, Guntur; Charvillat, Vincent; Ooi, Wei Tsang

doi:10.1145/2072298.2072306

Cited by 18 publications

(14 citation statements)

References 22 publications

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“…We use the RoI traces of 70 users who accessed a zoomable video system described in Carlier et al [3], and use these traces to compute tile access probabilities for each of the four video resolutions. The zoomable video system relies on multiple resolution videos with compressed domain RoI cropping in order to create a virtual zoom and pan effect.…”

Section: Discussionmentioning

confidence: 99%

On tile assignment for region-of-interest video streaming in a wireless LAN

Ravindra

Ooi

2012

Proceedings of the 22nd International Workshop on Network and Operating System Support for Digital Audio and Video

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We consider the following problem in this paper: A video is encoded as a set of tiles T and is streamed to multiple users via a onehop wireless LAN. Each user selects a region-of-interest (RoI), represented as a subset of T , in the video to watch. The RoI selected by the users may overlap. Each tile may be multicast or unicast. We define the tile assignment problem as: which subset of tiles should be multicast such that every user receives, within a transmission deadline, the subset of tiles pertaining to the RoI the user selected, while minimizing the number of unwanted tiles received by users. We present and evaluate five tile assignment methods. We show that: (i) minimizing transmission delay can lead to significant wasteful reception in the multicast group, (ii) using tile access probability to assign tiles frequently leads to assignments that violate the deadline, and (iii) a fast, greedy, heuristic works well: it performs close to the optimal method and can always find an assignment within the deadline (as long as such assignment exists).

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Section: Discussionmentioning

confidence: 99%

On tile assignment for region-of-interest video streaming in a wireless LAN

Ravindra

Ooi

2012

Proceedings of the 22nd International Workshop on Network and Operating System Support for Digital Audio and Video

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“…We implemented a tool which combines LikeLines [9], a zoomable video interface [4] and hand-drawn annotations [8]. The implementation was done in HTML5 and JavaScript 1 http://youtu.be/cqF_1TWKSsQ because of its multi-platform compatibility, which allows for deploying the tool on many di erent platforms (Windows, Linux, iOS) and devices (PCs, tablets, mobile phones).…”

Section: Methodsmentioning

confidence: 99%

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Riegler

Lux

Charvillat

et al. 2014

Proceedings of International Conference on Multimedia Retrieval

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Videos are becoming more and more a tool of communication. There are how-to videos, people are discussing actions of others based on their recorded performance, e.g., in soccer, or they simply record videos of great moments and show them to friends and family. In this paper we focus on very specific how-to videos and present a novel, web based annotation tool, that combines (i) zoom, (ii) drawing, and (iii) temporal social bookmarking in video streams. Moreover, we present a short study on the usefulness of the tool to communicate general concepts of a specific video game based on a captured game session.

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“…n.fluent employs both machine translation and online crowd to help translate documents 1 . Carlier et al combines content analysis and crowdsourcing to optimize the selection of video viewports [8]. While existing crowd-powered systems explicitly solicit a crowd's help (e.g., via Amazon Mechanical Turk) and use the results to help others, OpinionBlocks leverages its own users as the crowd implicitly, and motivates them to perform tasks that ultimately benefit both themselves and others (e.g., correctly identifying both positives and negatives of a product aspect).…”

Section: Crowd-powered Systemsmentioning

confidence: 99%

OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text

Yang

Zhou

et al. 2013

Lecture Notes in Computer Science

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Abstract. Millions of people rely on online opinions to make their decisions. To better help people glean insights from massive amounts of opinions, we present the design, implementation, and evaluation of OpinionBlocks, a novel interactive visual text analytic system. Our system offers two unique features. First, it automatically creates a fine-grained, aspect-based visual summary of opinions, which provides users with insights at multiple levels. Second, it solicits and supports user interactions to rectify text-analytic errors, which helps improve the overall system quality. Through two crowd-sourced studies on Amazon Mechanical Turk involving 101 users, OpinionBlocks demonstrates its effectiveness in helping users perform real-world opinion analysis tasks. Moreover, our studies show that the crowd is willing to correct analytic errors, and the corrections help improve user task completion time significantly. Keywords: Text analytics; text visualization; self-improving; crowd-sourcing Figure 1. The interface of OpinionBlocks: (a) a system-generated aspect-based summary; (b) a system-extracted review snippet; (c) the full text of a review.

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Combining content-based analysis and crowdsourcing to improve user interaction with zoomable video

Cited by 18 publications

References 22 publications

On tile assignment for region-of-interest video streaming in a wireless LAN

On tile assignment for region-of-interest video streaming in a wireless LAN

VideoJot

OpinionBlocks: A Crowd-Powered, Self-improving Interactive Visual Analytic System for Understanding Opinion Text

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