FIM: A Real-Time Content Based Sample Image Matching System

Luo, Yong; Li, Yangxi; Tu, Jinhui; Xu, Chao

doi:10.1109/icig.2013.116

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Moreover, we implement image retrieval algorithms [28], [29], [30] to enhance the performance of our system. Image retrieval mobile applications such as [28] are mainly designed for efficiency and speed.…”

Section: Introductionmentioning

confidence: 99%

“…An added advantage of our system is that we incorporate various levels of consecutive filtering to not only speed up processing but also reduce the number of candidates in each level according to the searching criteria desired and reported by the user. Real-time systems suffer from computational delays as discussed and improved on in [29]. A further advantage of our system to this is the consecutive filtering techniques that reduce the number of candidates run in the SURF image matching, significantly boosting the speed and performance of our proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Archival and Retrieval of Lost Objects using Multi-feature Image Matching in Mobile Applications

Ghazal¹

2016

IJCDS

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The rapid development of mobile computing technologies, as well as enhanced wireless communications, has paved the way for the development of the e-Government and m-Government systems. With the aim of increasing the accessibility of government services, such systems are still proven to be under-utilized, especially in the area of retrieving lost personal objects. In this paper, we propose a multiplatform mobile application for reporting and retrieval of lost objects in an efficient manner, rather than going through a manual procedure of filling up forms. The backend sever runs an object retrieval algorithm on the reported lost and found objects in the database, which is comprised of a textual search, a geographic information system filter, an image matching algorithm using Speeded Up Robust Features, followed by color validation using L*a*b* color space. Experiments have been performed on 40 lost objects against the database of 50 found objects with the accuracy of 95%, while providing a precision value of 100% and a 90% of recall. The efficiency and overall good performance of the proposed system can help in reducing the manual labor, as well as in providing fast feedback to the users.

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