Mobile Visual Search

Girod, Bernd; Chandrasekhar, Vijay; Chen, D M; Cheung, Ngai-Man; Grzeszczuk, Radek; Reznik, Yuriy A.; Takacs, Gabriel; Tsai, Shiou‐Chuan; Vedantham, Ramakrishna

doi:10.1109/msp.2011.940881

Cited by 272 publications

(180 citation statements)

References 50 publications

Supporting

Mentioning

178

Contrasting

Unclassified

Order By: Relevance

“…Image recognition mobile devices also has several successful examples such as Google Goggles, Amazon Snaptell etc, but most of these applications rely on a remote server for matching within large image databases. Some of these optimize on the network communication by sending compressed feature representations to the server [35,13,36,37]. Prior work on offline IR on mobile focusses on reducing memory footprint of search index and includes [14,[38][39][40].…”

Section: Example Use Casesmentioning

confidence: 99%

Optimizing Storage Intensive Vision Applications to Device Capacity

Girdhar

Panda

Jawahar

2015

Computer Vision -- ACCV 2014

View full text Add to dashboard Cite

Abstract. Computer vision applications today run on a wide range of mobile devices. Even though these devices are becoming more ubiquitous and general purpose, we continue to see a whole spectrum of processing and storage capabilities within this class. Moreover, even as the processing and storage capacity of devices are increasing, the complexity of vision solutions and the variety of use cases create greater demands on these resources. This requires appropriate adaptation of the mobile vision applications with minimal changes in the algorithm or implementation. In this work, we focus on optimizing the memory usage for storage intensive vision applications.In this paper, we propose a framework to configure memory requirements of vision applications. We start from a gold standard desktop application, and reduce the the size for a given the memory constraint. We formulate the storage optimization problem as mixed integer programming (mip) based optimization to select the most relevant subset of data to be retained. For large data sets, we use a greedy approximate solution which is empirically comparable to the optimal mip solution.We demonstrate the method in two different use cases: (a) Instance retrieval task where an image of a query object is looked up for instant recognition/annotation, and (b) Augmented reality where computational requirement is minimized by rendering and storing precomputed views. In both the cases, we show that our method allows a reduction in storage by almost 5× with no significant performance loss.

show abstract

Section: Example Use Casesmentioning

confidence: 99%

Optimizing Storage Intensive Vision Applications to Device Capacity

Girdhar

Panda

Jawahar

2015

Computer Vision -- ACCV 2014

View full text Add to dashboard Cite

show abstract

“…The delay caused by the communication between the client and the server using a typical 3G network has been studied in [5]. The transmission of 10 kB using the uplink of an indoor 3G connection takes about 5 to 8 seconds on average depending on the signal quality.…”

Section: Related Workmentioning

confidence: 99%

“…The application of CBIR to mobile location recognition raises new challenges when compared to mobile product recognition [5,6]. A severe amount of clutter and dynamic objects like cars, pedestrians, and advertisements can be found in both query and reference images.…”

Section: Introductionmentioning

confidence: 99%

Exploiting prior knowledge in mobile visual location recognition

Schroth

Huitl

Abu-Alqumsan

et al. 2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Mobile visual location recognition needs to be performed in realtime for location based services to be perceived as useful. We describe and validate an approach that eliminates the network delay by preloading partial visual vocabularies to the mobile device. Retrieval performance is significantly increased by composing partial vocabularies based on the uncertainty about the location of the client. This way, prior knowledge is efficiently integrated into the matching process. Based on compressed feature sets, infrequently uploaded from the mobile device, the server estimates the client location and its uncertainty by fusing consecutive query results using a particle filter.

show abstract

“…For each keypoint, a local feature is produced, summarizing the patch surrounding the keypoint. Since such features can be efficiently compressed and used for automatic analysis, they constitute an excellent candidate for enabling advanced visual tasks in energy-and bandwidthlimited scenarios [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

Bondi

Baroffio

Cesana

et al. 2016

IEEE Internet Things J.

View full text Add to dashboard Cite

Abstract-We present a complete, open-source framework for rapid experimentation of Visual Sensor Networks (VSN) solutions. From the software point of view, we base our architecture on open-source and widely known C++ libraries to provide the basic image processing and networking primitives. The resulting system can be leveraged to create different types of VSNs, characterized by the presence of multiple cameras, relays and cooperator nodes, and can be run on any Linux-based hardware platform, such as the BeagleBone Black. To demonstrate the flexibility of the proposed framework, we describe two different application scenarios typical of VSNs, namely object recognition and parking monitoring. The framework is then used to evaluate the benefits of two complementary paradigms for networked visual analysis recently discussed in the literature. In the traditional Compress-then-Analyze (CTA) paradigm, compressed images are transmitted from camera nodes to a central controller, where they are analyzed. In the novel Analyze-then-Compress (ATC) paradigm, camera nodes extracts and compress local features from the acquired images. Such features are transmitted to the central controller and used to perform visual analysis. We show that the ATC paradigm outperforms CTA from the consumed energy point of view, at the same target analysis accuracy in both the application scenarios.

show abstract

Mobile Visual Search

Cited by 272 publications

References 50 publications

Optimizing Storage Intensive Vision Applications to Device Capacity

Optimizing Storage Intensive Vision Applications to Device Capacity

Exploiting prior knowledge in mobile visual location recognition

EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

Contact Info

Product

Resources

About