CrowdPic: A Multi-coverage Picture Collection Framework for Mobile Crowd Photographing

Abstract

Abstract-Mobile Crowd Photographing (MCP) is attracting an emerging area of interest for researchers as cameras of mobile devices are becoming an indispensable visual logging device in people's everyday lives. In order to meet diverse MCP application constraints/requirements (e.g. where and when to sense, sampling frequency, single or multiple shooting angles) to sensing targets, a multi-facet task model should be defined as a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multi-constraint-driven data selection problem in MCP picture aggregation, and propose a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework called CrowdPic, we test and evaluate the effectiveness, efficiency and flexibility of the proposed framework through both crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

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“…To further explore the usefulness of our proposed model, we will investigate the application of our work to other online communities, such as Reddit, Quora, and Digg, to discover emotion leaders and followers. It is also our intention to develop a system like [5] based on our model, which could suggest a list of users for accomplishing a specific task, such as maximizing the message's positive propagation in the online social network.…”

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“…Picture set fusion is regarded as picture selection, and we will introduce some related works in the following. [2], such as the shooting direction, the timestamp, the location, and the ambient light. For example, FlierMeet [4] collects crowdsourced photos of fliers for cross-space public information reposting, tagging, and sharing, and it only selects one picture of every flier based on the visual feature.…”

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“…Because the function SIM returns f alse if any simf returns f alse in (2), we use the PTree-based clustering method [7], which is shown in Figure 2. In order to save computing cost, we set F = {location, timestamp, shooting direction, image} for FlierMeet [9], so pic = loc, tm, dir, img and the prior three thresholds for function simf are cLoc, cClo, cAng defined in Table 1 respectively.…”

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