Landmark Summarization With Diverse Viewpoints

Qian, Xueming; Xue, Yao; Yang, Xinyu; Tang, Yuan Yan; Hou, Xingsong; Mei, Tao

doi:10.1109/tcsvt.2014.2369731

Cited by 25 publications

(15 citation statements)

References 37 publications

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“…The parameters in our baseline algorithm are set as k = 50, and b = 60, and the size of BoW is set to be 60K [57]. We also discuss the impacts of using useful features or not, the selection of VWG's center and the different spatial constraint approaches.…”

Section: Discussionmentioning

confidence: 99%

“…1) The Impact of Using Useful Features: In the part of local feature refinement, we select useful features of images by tf-idf based approach, rather than the repeated visual words extracted from multiple queries [55]- [57]. For images, their visual words have different weights for location estimation.…”

Section: Discussionmentioning

confidence: 99%

“…Our image location estimation is purely based on image content. We can convert the image location estimation problem as content based image retrieval [25], [55]- [57], [64] or object recognition problem [58]- [61]. In social media community, the shared photos are always attached with tags, time stamps, user's comments, and geo-coordinates that images are taken.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Image Location Estimation by Salient Region Matching

Qian

Zhao

Han

2015

IEEE Trans. on Image Process.

Self Cite

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Nowadays, locations of images have been widely used in many application scenarios for large geo-tagged image corpora. As to images which are not geographically tagged, we estimate their locations with the help of the large geo-tagged image set by content-based image retrieval. In this paper, we exploit spatial information of useful visual words to improve image location estimation (or content-based image retrieval performances). We proposed to generate visual word groups by mean-shift clustering. To improve the retrieval performance, spatial constraint is utilized to code the relative position of visual words. We proposed to generate a position descriptor for each visual word and build fast indexing structure for visual word groups. Experiments show the effectiveness of our proposed approach.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Image Location Estimation by Salient Region Matching

Qian

Zhao

Han

2015

IEEE Trans. on Image Process.

Self Cite

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“…The POIs are not only applicable to user context (e.g., recent location, time interval and check-in) but personalised to check-in olden times. Xiyu Yang [10] Landmark summarization is actual significant in breakthrough retrieval has it creates a description for users. It characterises the view point for the image and select the demonstrative images with viewpoints.…”

Section: Literature Surveymentioning

confidence: 99%

Personalized Portable Travelogue Guide on Multi-Source Big Social Media

2017

IJAERD

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“…Compared with our preliminaries [8], [14], [44], [46], several enhancements have been made in this paper.…”

Section: Introductionmentioning

confidence: 99%

Scalable Mobile Image Retrieval by Exploring Contextual Saliency

Yang

Qian

Xue

2015

IEEE Trans. on Image Process.

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Nowadays, it is very convenient to capture photos by a smart phone. As using, the smart phone is a convenient way to share what users experienced anytime and anywhere through social networks, it is very possible that we capture multiple photos to make sure the content is well photographed. In this paper, an effective scalable mobile image retrieval approach is proposed by exploring contextual salient information for the input query image. Our goal is to explore the high-level semantic information of an image by finding the contextual saliency from multiple relevant photos rather than solely using the input image. Thus, the proposed mobile image retrieval approach first determines the relevant photos according to visual similarity, then mines salient features by exploring contextual saliency from multiple relevant images, and finally determines contributions of salient features for scalable retrieval. Compared with the existing mobile-based image retrieval approaches, our approach requires less bandwidth and has better retrieval performance. We can carry out retrieval with <200-B data, which is <5% of existing approaches. Most importantly, when the bandwidth is limited, we can rank the transmitted features according to their contributions to retrieval. Experimental results show the effectiveness of the proposed approach.

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Landmark Summarization With Diverse Viewpoints

Cited by 25 publications

References 37 publications

Image Location Estimation by Salient Region Matching

Image Location Estimation by Salient Region Matching

Personalized Portable Travelogue Guide on Multi-Source Big Social Media

Scalable Mobile Image Retrieval by Exploring Contextual Saliency

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