# The Minimum Selection of Crowdsourcing Images under the Resource Budget

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. The Minimum Selection Problem under Restriction of Resources

**Definition**

**1**

**(minimum**

**selection**

**problem).**

#### 3.1. Unit-Data

#### 3.2. Target Segment Coverage Model

#### 3.3. Coverage Utility

#### 3.4. Target Area Coverage Model

#### 3.5. Utility Calculation Method Evaluation

- The probability distribution in ($\mu -\sigma $, $\mu +\sigma $) is 0.6826.
- The numerical distribution rate is 0.9545.
- The probabilities in the probability distribution ($\mu -2\sigma $, $\mu +2\sigma $) in ($\mu -3s\sigma $, $\mu +3\sigma $) are 0.9973.

#### Evaluation Results

## 4. Minimum Selection Algorithm

#### 4.1. Problem Conversion

#### 4.2. Minimum Selection Problem Algorithm

**Theorem**

**1.**

**Proof.**

#### 4.3. k Times Coverage Based on Minimum Seletion

**Definition**

**2**

**(k**

**times**

**coverage).**

#### 4.4. Conclusions

## 5. Scene Experiment and Simulation

#### 5.1. Data-Unit Acquisition

#### 5.2. Occlusion and out of Focus

#### 5.3. Scenario Testing

- Minimum selection algorithm.
- Random Algorithm 1 based on position: select the candidate photos randomly.
- Random Algorithm 2: random selection in candidate photo sets.

#### 5.4. Simulation Experiment

#### 5.4.1. Simulation Results of Minimum Selection Algorithm

#### 5.4.2. k-Coverage Simulation Results

## 6. Accuracy Analysis

#### 6.1. GPS Acquisition

#### 6.2. Occlusion Problems

#### 6.3. Images Quality Control

## 7. Summary

**Research in the future:**

- Analyzing the information of photos and assigning different weights to them to make further selections. For example: If there are more survivors in a building during an emergency, such as a teaching building where exits obtained by photos should be selected preferably.
- Improving algorithm to eliminate redundancy of images, increasing the accuracy of collecting data-units and mitigating obstacle obstruction and out-of-focus issues.
- The computational efficiency of the algorithm could be enhanced, and take implementing the algorithm in a distributed computing way into consideration.

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Fang, W.; Wang, D.H.; Wang, Y. Energy-efficient distributed target tracking in wireless video sensor networks. Int. J. Wirel. Inf. Netw.
**2015**, 22, 105–115. [Google Scholar] [CrossRef] - Chen, F.; Zhang, C.; Wang, F.; Liu, J. Crowdsourced live streaming over the cloud. In Proceedings of the 2015 IEEE Conference on Computer Communications (INFOCOM), Kowloon, Hong Kong, 26 April–1 May 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 2524–2532. [Google Scholar] [Green Version]
- Jain, P.; Manweiler, J.; Acharya, A.; Beaty, K. FOCUS: clustering crowdsourced videos by line-of-sight. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, Roma, Italy, 11–15 November 2013; ACM: New York, NY, USA, 2013; p. 8. [Google Scholar]
- Tavoli, R.; Kozegar, E.; Shojafar, M.; Soleimani, H.; Pooranian, Z. Weighted PCA for improving Document Image Retrieval System based on keyword spotting accuracy. In Proceedings of the 2013 36th International Conference on Telecommunications and Signal Processing (TSP), Rome, Italy, 2–4 July 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 773–777. [Google Scholar]
- Phillips, P.J.; Scruggs, W.T.; O’Toole, A.J.; Flynn, P.J.; Bowyer, K.W.; Schott, C.L.; Sharpe, M. FRVT 2006 and ICE 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell.
**2010**, 32, 831–846. [Google Scholar] [CrossRef] [PubMed] - Lee, D.; Plataniotis, K.N. Toward a no-reference image quality assessment using statistics of perceptual color descriptors. IEEE Trans. Image Process.
**2016**, 25, 3875–3889. [Google Scholar] [CrossRef] [PubMed] - Peng, D.; Wu, F.; Chen, G. Pay as how well you do: A quality based incentive mechanism for crowdsensing. In Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Hangzhou, China, 22–25 June 2015; ACM: New York, NY, USA, 2015; pp. 177–186. [Google Scholar]
- Simoens, P.; Xiao, Y.; Pillai, P.; Chen, Z.; Ha, K.; Satyanarayanan, M. Scalable crowd-sourcing of video from mobile devices. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, Taipei, Taiwan, 25–28 June 2013; ACM: New York, NY, USA, 2013; pp. 139–152. [Google Scholar] [Green Version]
- Goodfellow, I.J.; Bulatov, Y.; Ibarz, J.; Arnoud, S.; Shet, V. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv, 2013; arXiv:1312.6082. [Google Scholar]
- Xiao, J.; Quan, L. Multiple view semantic segmentation for street view images. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 29 September–2 October 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 686–693. [Google Scholar]
- LiKamWa, R.; Zhong, L. Starfish: Efficient concurrency support for computer vision applications. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, Florence, Italy, 18–22 May 2015; ACM: New York, NY, USA, 2015; pp. 213–226. [Google Scholar]
- Yang, D.; Xue, G.; Fang, X.; Tang, J. Incentive Mechanisms for Crowdsensing: Crowdsourcing with Smartphones. IEEE/ACM Trans. Netw.
**2016**, 24, 1732–1744. [Google Scholar] [CrossRef] - Wang, Y.; Cao, G. On full-view coverage in camera sensor networks. In Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China, 10–15 April 2011; pp. 1781–1789. [Google Scholar] [CrossRef]
- Hasna, M.O.; Alouini, M.S. Harmonic mean and end-to-end performance of transmission systems with relays. Commun. IEEE Trans.
**2004**, 52, 130–135. [Google Scholar] [CrossRef] - Shen, C.; Bao, X.; Tan, J.; Liu, S.; Liu, Z. Two noise-robust axial scanning multi-image phase retrieval algorithms based on Pauta criterion and smoothness constraint. Opt. Express
**2017**, 25, 16235. [Google Scholar] [CrossRef] [PubMed] - Arora, S. Approximation schemes for NP-hard geometric optimization problems: A survey. Math. Programm.
**2003**, 97, 43–69. [Google Scholar] [CrossRef] - Hörster, E.; Lienhart, R. On the optimal placement of multiple visual sensors. In Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks, Santa Barbara, CA, USA, 27 October 2006; ACM: New York, NY, USA, 2006; pp. 111–120. [Google Scholar]
- Dobson, G. Worst-case analysis of greedy heuristics for integer programming with nonnegative data. Math. Oper. Res.
**1982**, 7, 515–531. [Google Scholar] [CrossRef] - Gabarda, S.; Cristóbal, G. Blind image quality assessment through anisotropy. OSA Publ.
**2007**, 24, B42–B51. [Google Scholar] [CrossRef] - Wan, J.; Wang, D.; Hoi, S.C.H.; Wu, P.; Zhu, J.; Zhang, Y.; Li, J. Deep learning for content-based image retrieval: A comprehensive study. In Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA, 3–7 November 2014; ACM: New York, NY, USA, 2014; pp. 157–166. [Google Scholar]
- Uddin, M.Y.S.; Wang, H.; Saremi, F.; Qi, G.J.; Abdelzaher, T.; Huang, T. Photonet: A similarity-aware picture delivery service for situation awareness. In Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium (RTSS), Vienna, Austria, 29 November–2 December 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 317–326. [Google Scholar]
- SensorManager—Android Developer. 15–20 May 2014. Available online: http://developer.android.com/reference/android/hardware/Sensor- Manager.html (accessed on 1 July 2018).
- Chen, S.; Li, M.; Ren, K.; Qiao, C. Crowd map: Accurate reconstruction of indoor floor plans from crowdsourced sensor-rich videos. In Proceedings of the 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS), Columbus, OH, USA, 29 June–2 July 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1–10. [Google Scholar]
- Yan, T.; Kumar, V.; Ganesan, D. Crowdsearch: Exploiting crowds for accurate real-time image search on mobile phones. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, San Francisco, CA, USA, 15–18 June 2010; ACM: New York, NY, USA, 2010; pp. 77–90. [Google Scholar]
- Li, G.; Fan, J. Crowdsourced Data Management: Overview and Challenges. In Proceedings of the ACM International Conference on Management of Data, Chicago, IL, USA, 14–19 May 2017; pp. 1711–1716. [Google Scholar]
- Ray, S.F.; Gates, W.C. Applied Photographic Optics. J. Photogr. Sci.
**2002**, 42, 140. [Google Scholar] [CrossRef] - Meier, R. Professional Android 2 Application Development, 2nd ed.; Wiley: New York, NY, USA, 2010. [Google Scholar]
- Crandall, D.; Snavely, N. Modeling people and places with internet photo collections. Queue
**2012**, 10, 30. [Google Scholar] [CrossRef] - Akyildiz, I.F.; Melodia, T.; Chowdhury, K.R. A survey on wireless multimedia sensor networks. Comput. Netw.
**2007**, 51, 921–960. [Google Scholar] [CrossRef] [Green Version] - Lee, G.Y.; Yun, N.Y.; Lee, S.C.; Park, S.H. A smart electronic tagging system based on context awareness and machine-to-machine interworking. Int. J. Distrib. Sens. Netw.
**2013**, 9, 392083. [Google Scholar] [CrossRef] - Johnson, M.P.; Bar-Noy, A. Pan and scan: Configuring cameras for coverage. In Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China, 10–15 April 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 1071–1079. [Google Scholar]
- Zandbergen, P.A.; Barbeau, S.J. Positional accuracy of assisted GPS data from high-sensitivity GPS-enabled mobile phones. J. Navig.
**2011**, 64, 381–399. [Google Scholar] [CrossRef] - Wang, Y.; Cao, G. Achieving full-view coverage in camera sensor networks. ACM Trans. Sens. Netw. (ToSN)
**2013**, 10, 3. [Google Scholar] [CrossRef] - Gao, R.; Zhao, M.; Ye, T.; Ye, F.; Wang, Y.; Bian, K.; Wang, T.; Li, X. Jigsaw: Indoor floor plan reconstruction via mobile crowdsensing. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, Maui, HI, USA, 7–11 September 2014; ACM: New York, NY, USA, 2014; pp. 249–260. [Google Scholar]
- Chow, E.; Vassilevski, P.S. Numerical Linear Algebra With Applications. Canadian Mathematical Bulletin; Wiley-Blackwell: Hoboken, NJ, USA, 2002; pp. 361–386. [Google Scholar]
- Wu, W.; Guo, B.; Yu, Z. Urban noise detection and analysis of spatio-temporal patterns based on group-wise perception. J. Comput.-Aided Des. Comput. Graph.
**2014**, 26, 638–643. [Google Scholar] - Feng, J.; Li, G.; Feng, J. A Survey of Crowdsourcing Technology Research. J. Electron. Meas. Instrum.
**2015**, 38, 1713–1726. [Google Scholar]

**Figure 12.**The locations and orientations of the photos: (

**a**) minimum selection; (

**b**) random1; (

**c**) random2.

**Figure 13.**The gradient diagram of three algorithms. (

**a**) Minimum selection algorithm; (

**b**) Random Algorithm 1; (

**c**) Random Algorithm 2.

**Figure 14.**Simulation on selecting photo under the 3 different factors. (

**a**) Photo selected vs. related $\u03f5=60,G=1250$ M; (

**b**) Photo selected vs. related $\u03f5=60,I=280$; (

**c**) Photo selected vs. related $G=125$ M, $I=280$.

**Figure 15.**Simulation results on Minimum selection algorithm. (

**a**) Photo selected vs. related $n=20$, $\u03f5=60,\phantom{\rule{3.33333pt}{0ex}}\lambda =100$; (

**b**) Photo selected vs. targets to be covered $\u03f5=60,\phantom{\rule{3.33333pt}{0ex}}m=500$; (

**c**) Photo selected vs. aspects to be covered $\u03f5=60,\phantom{\rule{3.33333pt}{0ex}}m=500,\phantom{\rule{3.33333pt}{0ex}}n=20$.

**Figure 16.**Simulation results on minimum selection with k-coverage. (

**a**) Photos selected vs. photos available $n=20,\phantom{\rule{3.33333pt}{0ex}}\u03f5=60,\phantom{\rule{3.33333pt}{0ex}}\lambda =100$; (

**b**) Photo selected vs. targets to be covered $\u03f5=60,\phantom{\rule{3.33333pt}{0ex}}m=500,\phantom{\rule{3.33333pt}{0ex}}\lambda =100$; (

**c**) Photo selected vs. aspects to be covered $\u03f5=60,\phantom{\rule{3.33333pt}{0ex}}m=500,\phantom{\rule{3.33333pt}{0ex}}n=20,\phantom{\rule{3.33333pt}{0ex}}\lambda =100$.

Parameter | Meaning |
---|---|

$\overrightarrow{d}$ | A vector which is emitted from the camera to the plane of the image |

$\lambda $ | The field of view of the camera lens |

r | The camera’s effective range of shooting |

p | The position of the camera |

$\alpha $ | The triangular angle between the camera and the target segment |

${\beta}_{i}$ | The size of an arbitrary edge angle of the triangle formed by a camera and the target segment |

$\mathsf{\Gamma}$ | A set of target areas |

F | A set of photos |

Rekated Property Item | Configuration Information | Remarks |
---|---|---|

Operating system | Windows 10 64 bit | Uses the window system for compatibility with software tools |

Running memory | 8192 MB RAM | Increased running time |

Processor | Intel Core i9-7900X | N/A |

Manufacturer | Lenovo | N/A |

Hard disk | 500 GB | Access to a large number of experimental data |

Input: | The angle of the photos: ${\mathit{F}}_{1}({\mathit{\theta}}_{1}),{\mathit{F}}_{2}({\mathit{\theta}}_{2}),\mathit{\dots},{\mathit{F}}_{\mathit{m}}({\mathit{\theta}}_{\mathit{m}})$ The related parameters of the camera |

Parameter: | Effective angle: ϵ |

Step: | |

1: | For $i\leftarrow 1$ to $i\leftarrow n$ do |

2: | //To assign a value to the form of an interval |

3: | ${F}_{i}\leftarrow {F}_{i}({\theta}_{i}-\u03f5,{\theta}_{i}+\u03f5);$ |

4: | $U\leftarrow {F}_{i}$; //Adding the molecular interval to the interval set U |

5: | End for |

6: | //The partition of the interval |

7: | While ${u}_{1}$ to ${u}_{k}$ do |

//Ergodic interval U | |

8: | //Merging sequence: ${e}_{1},{e}_{2}\dots {e}_{j}$; |

9: | For $r\leftarrow 1$ to $r\leftarrow j$ do |

10: | //In the polar coordinate system, it is transformed into the form of sub intervals of elements through the neighborhood principle. |

11: | //Sort, get a new sequence of E |

12: | End for |

//End the sortation and return to the ordered sequence E | |

13: | //Transform into ${S}_{t}$ interval cover form |

14: | ${S}_{t}\left\{{e}_{1}^{\prime},{e}_{2}^{\prime}\dots {e}_{h}^{\prime}\right\}\leftarrow {u}_{h}$; |

15: | End while |

16: | //Traversing the new interval set S to select the most efficient combination of photos. |

17: | Repeat |

18: | If it contains the most multiple interval then |

19: | Add to the final photo set |

20: | End if |

21: | Until all the elements are traversed |

22: | Output the final selection ${S}_{result\phantom{\rule{0.277778em}{0ex}}set}$ |

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**MDPI and ACS Style**

Song, J.; Zhao, M.; Long, S.
The Minimum Selection of Crowdsourcing Images under the Resource Budget. *Symmetry* **2018**, *10*, 256.
https://doi.org/10.3390/sym10070256

**AMA Style**

Song J, Zhao M, Long S.
The Minimum Selection of Crowdsourcing Images under the Resource Budget. *Symmetry*. 2018; 10(7):256.
https://doi.org/10.3390/sym10070256

**Chicago/Turabian Style**

Song, Jieqiong, Ming Zhao, and Sifan Long.
2018. "The Minimum Selection of Crowdsourcing Images under the Resource Budget" *Symmetry* 10, no. 7: 256.
https://doi.org/10.3390/sym10070256