Visual summarization of landmarks via viewpoint modeling

Xue, Yao; Qian, Xueming

doi:10.1109/icip.2012.6467499

Cited by 17 publications

(20 citation statements)

References 6 publications

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“…As in [8], detecting ISP needs to match SIFT features between every two images. For one local feature in an image, it is matched with all the features in other images to detect the optimal matched pair.…”

Section: Detecting Identical Semantic Pointmentioning

confidence: 99%

“…We mine salient visual word based on identical semantic point (ISP) detection in our previous work [8]. As in [8], detecting ISP needs to match SIFT features between every two images.…”

Section: Detecting Identical Semantic Pointmentioning

confidence: 99%

“…Following [8], then we perform optimal matching pair determination between every two images in multi-images to capture repeated content. During each image-image match, we record all the optimal matched SIFT points pairs (u,q) and their matching scores MS(u,q).…”

Section: Figure 2 the Comparison Between Raw Sift Features And Extramentioning

confidence: 99%

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Spatial Verification for Scalable Mobile Image Retrieval

Yang

Qian

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Self Cite

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Owing to the portable and excellent phone camera, people now prefer to take photos and upload them by mobile phone. Content based image retrieval is effective for users to obtain relevant information about a photo. Taking the limited bandwidth and instability into account, we propose an effective scalable mobile image retrieval approach in this paper. The proposed mobile image retrieval algorithm first determines the relevant photos according to visual similarity in mobile end, then mines salient visual words by exploring saliency from multiple relevant images, and finally we determine the contribution order of salient visual words for scalable retrieval. In server terminal, spatial verification is performed to re-rank the results. Compared to the existing approaches of mobile image retrieval, our approach transmits less data and reduces the computational cost of spatial verification. Most importantly, when the bandwidth is limited, we can transmit a part of features according their contributions to retrieval. Experimental results show the effectiveness of the proposed approach.

show abstract

Section: Detecting Identical Semantic Pointmentioning

confidence: 99%

“…We mine salient visual word based on identical semantic point (ISP) detection in our previous work [8]. As in [8], detecting ISP needs to match SIFT features between every two images.…”

Section: Detecting Identical Semantic Pointmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Verification for Scalable Mobile Image Retrieval

Yang

Qian

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Self Cite

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“…It displays a picture's perspective utilizing a 4-D perspective vector, which portrays perspective in level, vertical, scale and introduction angles. Ren et al [15] create visual rundown for POI. (Purpose Of-Interest) through LOI (Location-Of-Interest) mining.…”

Section: Related Workmentioning

confidence: 99%

RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues

Maheswari¹,

Sathiaseelan²

2018

IJET

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The travelogues contain prosperous geo-referenced information such as tours, weather, and expenses etc. Reading travelogues and finding useful trip information is a tedious task for many people. So as to correlate the georeferenced text meaningfully the semantic ranking is taken in account on the user contributing travelogues. In this way there is a requirement for creating programmed travelogue mining methods to pass on valuable data in a travelogue to its peruses in the more successful way. Visualizing a geo-referenced data with location tags and descriptions makes it helpful for readers to understand the fundamental substance of the travelogue. For user expedient, this paper proposes a Travelogue RankTagViz approach that semantically ranks and visualizes the tags of user travelogues. The RankTagViz contains two phases. In the first phase, the user travelogues are ranked based on semantic. In the next phase, the travelogues are visualized based on the tag and images. During semantic ranking, a semantic dimension reduction method is proposed to pre-process and mine useful information from the travelogues. After that a semantic rank mechanism is proposed to rank travelogues based on tags and POI (Point Of Interest). For tag visualization, location based tags and images are extracted from the travelogues and a novel UI is intended to give a superior client encounter, by arranging both the literary and visual data produced by committed voyagers in an alluring way. Test comes about on an arrangement of gathered travelogues show the proposed techniques' capacity to rank and imagine travelogues.

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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.

show abstract

Visual summarization of landmarks via viewpoint modeling

Cited by 17 publications

References 6 publications

Spatial Verification for Scalable Mobile Image Retrieval

Spatial Verification for Scalable Mobile Image Retrieval

RankTagViz: A Semantic Ranking and Tags Visualization of User Travelogues

Scalable Mobile Image Retrieval by Exploring Contextual Saliency

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