Paper Info
Title
Resource-limited intelligent photo management on mobile platforms
Abstract
Apart from wireless communication issues, a key technical challenge is how to achieve the best-experienced photo browsing given the limited screen size of the mobile devices. Therefore, in this paper, we propose a novel technique, resource-limited intelligent photo management (RIPM), on the demand of reducing the complexity of computation on Android mobile platform, in which photos captured are analyzed directly in JPEG compressed domain and are further classified in a real-time manner based on the human subject's gender. In order to make the system robust to luminance variations, DC coefficients are discarded. In addition, for the low-complexity purpose and the effective gender discrimination, a set of AC coefficients are selected automatically based on a three-step dimensionality reduction, in which evaluation of the coefficients' significance is conducted by LDA-based approach. Experimental results obtained by using extensive dataset captured under uncontrolled environments show that our system is effective for photo managements on resource-limited mobile platform.
Year
DOI
Venue
2011
10.1109/ICMLC.2011.6016796
ICMLC
Keywords
Field
DocType
wireless communication issues,resource limited intelligent photo management,best experienced photo browsing,lda based approach,android mobile platform,ac coefficients,data compression,gender recognition,operating systems (computers),content management,computational complexity,jpeg compressed domain,computation complexity,lda,dc coefficients,mobile platform,dct,photo management,luminance variations,mobile computing,gender discrimination,discrete cosine transform,feature extraction,face recognition,transform coding,mobile device,mobile communication,wireless communication,real time,face
Mobile computing,Wireless,Display size,Computer science,Real-time computing,Artificial intelligence,Content management,Computer vision,Android (operating system),JPEG,Mobile device,Data compression,Machine learning
Conference
Volume
ISSN
ISBN
2
2160-133X
978-1-4577-0305-8
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Duan-Yu Chen129628.79
Jeng-Tsung Tsai210.69