Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
PhoneGuide: museum guidance supported by on-device object recognition on mobile phones
MUM '05 Proceedings of the 4th international conference on Mobile and ubiquitous multimedia
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Icandy: a tangible user interface for itunes
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Outdoors augmented reality on mobile phone using loxel-based visual feature organization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Tree Histogram Coding for Mobile Image Matching
DCC '09 Proceedings of the 2009 Data Compression Conference
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Low-rate image retrieval with tree histogram coding
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Towards low bit rate mobile visual search with multiple-channel coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Compressed Histogram of Gradients: A Low-Bitrate Descriptor
International Journal of Computer Vision
Learning compact visual descriptor for low bit rate mobile landmark search
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Geometry directed browser for personal photographs
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Geometry directed browser for personal photographs
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
| Hi-index | 0.00 |
In this paper, we demonstrate a computer vision application on mobile phones. One can take a picture at a heritage site/monument and obtain associated annotations on a mid-end mobile phone instantly. This does not require any communication of images or features with a remote server, and all the necessary computations take place on the phone itself. We demonstrate the app on two Indian heritage sites: Golkonda Fort and Hampi Temples. Underlying our application, we have a Bag of visual Words (BoW) image retrieval system, and an annotated database of images. In the process of developing this mobile app, we extend the performance, scope and applicability of computer vision techniques: (i) we do a BoW-based image retrieval on mobile phones from a database of 10K images within 50 MB of storage and 10 MB of RAM. (ii) we introduce a vocabulary pruning method for reducing the vocabulary size. (iii) we design a simple method of database pruning, that helps in reducing the size of the inverted index by removing semantically similar images. In (ii) and (iii), we demonstrate how memory(RAM) and computational speed can be optimized without any loss in performance.