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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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Location recognition results.(a), (b), (c), (d) and (e) are achieved by the method of [5]. (f), (g), (h), (i) and (j) are achieved by our proposed method. The image marked with red “X” denotes wrong result.
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pone-0098806-g006: Location recognition results.(a), (b), (c), (d) and (e) are achieved by the method of [5]. (f), (g), (h), (i) and (j) are achieved by our proposed method. The image marked with red “X” denotes wrong result.

Mentions: We also implement the method proposed by Chen et al. [5] for comparison. Figure 6 gives the comparison results. We can see that our method outperforms the method of [5]. The method of [5] fails in many query examples, while our method works well.


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)

Location recognition results.(a), (b), (c), (d) and (e) are achieved by the method of [5]. (f), (g), (h), (i) and (j) are achieved by our proposed method. The image marked with red “X” denotes wrong result.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0098806-g006: Location recognition results.(a), (b), (c), (d) and (e) are achieved by the method of [5]. (f), (g), (h), (i) and (j) are achieved by our proposed method. The image marked with red “X” denotes wrong result.
Mentions: We also implement the method proposed by Chen et al. [5] for comparison. Figure 6 gives the comparison results. We can see that our method outperforms the method of [5]. The method of [5] fails in many query examples, while our method works well.

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