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On-device mobile visual location recognition by using panoramic images and compressed sensing based visual descriptors.

Guan T, Fan Y, Duan L, Yu J - PLoS ONE (2014)

Bottom Line: Secondly, to search high dimensional visual descriptors directly on mobile devices, we propose an effective bilinear compressed sensing based encoding method.While being fast and accurate enough for on-device implementation, our algorithm can also reduce the memory usage of projection matrix significantly.Experimental results prove the effectiveness of the proposed methods for on-device MVLR applications.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Huazhong University of Science & Technology, Wuhan, People's Republic of China.

ABSTRACT
Mobile Visual Location Recognition (MVLR) has attracted a lot of researchers' attention in the past few years. Existing MVLR applications commonly use Query-by-Example (QBE) based image retrieval principle to fulfill the location recognition task. However, the QBE framework is not reliable enough due to the variations in the capture conditions and viewpoint changes between the query image and the database images. To solve the above problem, we make following contributions to the design of a panorama based on-device MVLR system. Firstly, we design a heading (from digital compass) aware BOF (Bag-of-features) model to generate the descriptors of panoramic images. Our approach fully considers the characteristics of the panoramic images and can facilitate the panorama based on-device MVLR to a large degree. Secondly, to search high dimensional visual descriptors directly on mobile devices, we propose an effective bilinear compressed sensing based encoding method. While being fast and accurate enough for on-device implementation, our algorithm can also reduce the memory usage of projection matrix significantly. Thirdly, we also release a panoramas database as well as a set of test panoramic quires which can be used as a new benchmark to facilitate further research in the area. Experimental results prove the effectiveness of the proposed methods for on-device MVLR applications.

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Related in: MedlinePlus

Illustration of heading-aware method for query.
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pone-0098806-g005: Illustration of heading-aware method for query.

Mentions: Check the area's endpoints. If the query in this part is less than half of it, the endpoint part is excluded. As shown in Figure 5, the query's range corresponds to the parts 1 to 4. Check the endpoint part 1 and part 4, and the part 1 is excluded. For each included part of query, extract the local image features and generate sub-descriptor. Suppose that the included parts are . The heading-aware descriptor of query is concatenated by n sub-descriptors:


On-device mobile visual location recognition by using panoramic images and compressed sensing based visual descriptors.

Guan T, Fan Y, Duan L, Yu J - PLoS ONE (2014)

Illustration of heading-aware method for query.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4043852&req=5

pone-0098806-g005: Illustration of heading-aware method for query.
Mentions: Check the area's endpoints. If the query in this part is less than half of it, the endpoint part is excluded. As shown in Figure 5, the query's range corresponds to the parts 1 to 4. Check the endpoint part 1 and part 4, and the part 1 is excluded. For each included part of query, extract the local image features and generate sub-descriptor. Suppose that the included parts are . The heading-aware descriptor of query is concatenated by n sub-descriptors:

Bottom Line: Secondly, to search high dimensional visual descriptors directly on mobile devices, we propose an effective bilinear compressed sensing based encoding method.While being fast and accurate enough for on-device implementation, our algorithm can also reduce the memory usage of projection matrix significantly.Experimental results prove the effectiveness of the proposed methods for on-device MVLR applications.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Technology, Huazhong University of Science & Technology, Wuhan, People's Republic of China.

ABSTRACT
Mobile Visual Location Recognition (MVLR) has attracted a lot of researchers' attention in the past few years. Existing MVLR applications commonly use Query-by-Example (QBE) based image retrieval principle to fulfill the location recognition task. However, the QBE framework is not reliable enough due to the variations in the capture conditions and viewpoint changes between the query image and the database images. To solve the above problem, we make following contributions to the design of a panorama based on-device MVLR system. Firstly, we design a heading (from digital compass) aware BOF (Bag-of-features) model to generate the descriptors of panoramic images. Our approach fully considers the characteristics of the panoramic images and can facilitate the panorama based on-device MVLR to a large degree. Secondly, to search high dimensional visual descriptors directly on mobile devices, we propose an effective bilinear compressed sensing based encoding method. While being fast and accurate enough for on-device implementation, our algorithm can also reduce the memory usage of projection matrix significantly. Thirdly, we also release a panoramas database as well as a set of test panoramic quires which can be used as a new benchmark to facilitate further research in the area. Experimental results prove the effectiveness of the proposed methods for on-device MVLR applications.

Show MeSH
Related in: MedlinePlus