With recent advances in mobile computing, the demand for visual localization or landmark identification on mobile devices is gaining interest. We advance the state of the art in this area by fusing two popular representations of street-level image data - facade-aligned and viewpoint-aligned - and show that they contain complementary information that can be exploited to significantly improve the recall rates on the city scale. We also improve feature detection in low contrast parts of the street-level data, and discuss how to incorporate priors on a user's position (e.g. given by noisy GPS readings or network cells), which previous approaches often ignore. Finally, and maybe most importantly, we present our results according to a carefully designed, repeatable evaluation scheme and make publicly available a set of 1.7 million images with ground truth labels, geotags, and calibration data, as well as a difficult set of cell phone query images. We provide these resources as a benchmark to facilitate further research in the area.

We survey popular data sets used in computer vision literature and point out their limitations for mobile visual search applications. To overcome many of the limitations, we propose the Stanford Mobile Visual Search data set. The data set contains camera-phone images of products, CDs, books, outdoor landmarks, business cards, text documents, museum paintings and video clips. The data set has several key characteristics lacking in existing data sets: rigid objects, widely varying lighting conditions, perspective distortion, foreground and background clutter, realistic ground-truth reference data, and query data collected from heterogeneous low and high-end camera phones. We hope that the data set will help push research forward in the field of mobile visual search.

In this paper, they address a key challenge for scaling image search up to larger databases: the amount of memory consumed by the inverted index. In a VT-based image retrieval system, the most memory-intensive structure is the inverted index. For example, in a database of one million images where each image contains hundreds of features, the inverted index consumes 2.5 GB of RAM. Such large memory usage limits the ability to run other concurrent processes on the same server, such as recognition systems for other databases. A memory-congested server can exhibit swapping between main and virtual memory, which significantly slows down all processes.

In this paper, we focus on a new application of mobile visual search: snapping a photo with a mobile device of a video playing on a TV screen to automatically retrieve and stream the remainder of the video to the mobile device. When the user takes a photo of the video, the captured query frame may contain too few useful features for good retrieval performance. We design and implement a new algorithm for mobile video retrieval to accurately select a feature-rich frame from a sequence of viewfinder frames in a very short temporal window determined by the user-initiated query event. Fast and accurate selection using efficiently computed Hessian scores is developed for real-time operation on mobile devices. Viewfinder frames captured before the query starts are pre-processed, while the number of viewfinder frames captured afterwards is minimized by a probabilistic optimization process. Evaluated on a large video database of 10 million frames, dynamic query frame selection provides a substantial increase in retrieval accuracy with very low search latency.

We present a fast and efficient geometric re-ranking method that can be incorporated in a feature based image-based retrieval system that utilizes a Vocabulary Tree (VT). We form feature pairs by comparing descriptor classification paths in the VT and calculate geometric similarity score of these pairs. We propose a location geometric similarity scoring method that is invariant to rotation, scale, and translation, and can be easily incorporated in mobile visual search and augmented reality systems. We compare the performance of the location geometric scoring scheme to orientation and scale geometric scoring schemes. We show in our experiments that re-ranking schemes can substantially improve recognition accuracy. We can also reduce the worst case server latency up to 1 sec and still improve the recognition performance.

We present a mobile product recognition system for the camera-phone. By snapping a picture of a product with a camera-phone, the user can retrieve online information of the product. The product is recognized by an image-based retrieval system located on a remote server. Our database currently comprises more than one million entries, primarily products packaged in rigid boxes with printed labels, such as CDs, DVDs, and books. We extract low bit-rate descriptors from the query image and compress the location of the descriptors using location histogram coding on the camera-phone. We transmit the compressed query features, instead of a query image, to reduce the transmission delay. We use inverted index compression and fast geometric re-ranking on our database to provide a low delay image recognition response for large scale databases. Experimental timing results on different parts of the mobile product recognition system is reported in this work.

Continuous recognition and tracking of objects in live video captured on a mobile device enables real-time user interaction. We demonstrate a streaming mobile augmented reality system with 1 second latency. User interest is automatically inferred from camera movements, so the user never has to press a button. Our system is used to identify and track book and CD covers in real time on a phone's viewfinder. Efficient motion estimation is performed at 30 frames per second on a phone, while fast search through a database of 20,000 images is performed on a server.

Mobile phones are an attractive platform for landmark-based pedestrian navigation systems. To be practical, such a system must be able to automatically generate lightweight directions that can be displayed on these mobile devices. We present a system that leverages an online collection of geotagged photographs to automatically generate navigational instructions. These are presented to the user as a sequence of images of landmarks augmented with directional instructions. Both the landmark selection and image augmentation are done automatically. We present a user study that indicates these generated directions are beneficial to users and suggest areas for future improvement.

'Rich media' is a term that implies the integration of all of the advances we have made in the mobile space delivering music, speech, text, graphics and video. This is true, but it is more than the sum of its parts. Rich media is the ability to deliver these modalities, to interact with these modalities, and to do it in a way that allows for the construction, delivery and use of compelling mobile services in an effective and economic manner. In this paper, we introduce a system called mobile open rich-media environment ('MORE') that helps realize such mobile rich media services, combining various technologies of W3C, OMA, 3GPP and IETF standards. The different components of the system include formatting, packaging, transporting, rendering and interacting with rich media files and streams

Header compression saves bandwidth, but it also introduces error propagation whenever packets are lost. We propose to use error correcting codes on the compressed packet headers. The result is an overall system that maintains most of the bandwidth savings of header compression and yet is robust with respect to errors. The key to achieving this tradeoff is appropriate distribution of parity symbols across the packets. The proposed system performs better than ordinary header compression and as well as the TWICE algorithm. In most cases, the effects of the error propagation can be almost removed, such that the end-to-end packet loss rate is similar to a system with no header compression, while the bitrate savings are largely maintained.