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

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.

[1]  Julien Rabin,et al.  An Analysis of the SURF Method , 2015, Image Process. Line.

[2]  Liu Li,et al.  Improved SIFT performance evaluation against various image deformations , 2014, 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference.

[3]  Masatsugu Kidode,et al.  Image matching robust to changes in imaging conditions with a car-mounted Camera , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[4]  K.Velmurugan,et al.  Content-Based Image Retrieval using SURF and Colour Moments , 2011 .

[5]  Bheemarjuna Reddy Tamma,et al.  Microsegmenting: An approach for precise distance calculation for GPS based its applications , 2013, 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS).

[6]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[7]  Rozilawati Razali,et al.  A preliminary review of interface design elements for mobile electronic government systems , 2014, 2014 4th World Congress on Information and Communication Technologies (WICT 2014).

[8]  Shihong Lao,et al.  Evaluation of color spaces for person re-identification , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[9]  Chengdong Wu,et al.  Rotation and affine-invariant SIFT descriptor for matching UAV images with satellite images , 2014, Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference.

[10]  Bo Li,et al.  Image matching using adapted image models and its application to content-based image retrieval , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[11]  Rizalafande Che Ismail,et al.  Digital Image Processing Techniques for Object Detection From Complex Background Image , 2012 .

[12]  Yong Luo,et al.  FIM: A Real-Time Content Based Sample Image Matching System , 2013, 2013 Seventh International Conference on Image and Graphics.

[13]  Pankaj Verma,et al.  Design and Development of GPS-GSM Based Tracking System with Google Map Based Monitoring , 2013 .

[14]  Yue Lu,et al.  Novel Global and Local Features for Near-Duplicate Document Image Matching , 2014, 2014 22nd International Conference on Pattern Recognition.

[15]  Krystian Mikolajczyk,et al.  Deep correlation for matching images and text , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.

[17]  Yue Lu,et al.  Variable-Length Signature for Near-Duplicate Image Matching , 2015, IEEE Transactions on Image Processing.

[18]  Keiichi Uchimura,et al.  Image matching by eigen template method for multi-class classification , 2015, 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV).

[19]  Zhang Qian,et al.  A novel image matching algorithm using local description , 2014, 2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP).

[20]  Mike McDonald,et al.  The potential application of GPS for distance controlled car following , 2002 .

[21]  Chih-Yang Lin,et al.  Fast image matching using multi-level texture descriptor , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

[22]  Panagiotis Sidiropoulos,et al.  Matching of Large Images Through Coupled Decomposition , 2015, IEEE Transactions on Image Processing.

[23]  Yongchen Li,et al.  An efficient image matching method using Speed Up Robust Features , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[24]  Aimin Zhou,et al.  Image segmentation framework based on multiple feature spaces , 2015, IET Image Process..

[25]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[26]  Zhaohui Li,et al.  An image matching method based on the analysis of grey correlation degree and feature points , 2014, NAECON 2014 - IEEE National Aerospace and Electronics Conference.