The Mobile Visual Search Guiding System Based on SIFT

Xu, Gongwen; Li, Xiaomei; Zhou, Huaqiang; Jiang, Lei; Zhang, Zhijun

doi:10.14257/ijfgcn.2016.9.6.16

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In addition to global features such as color, texture and shape, many local feature descriptors are proposed, including SIFT (Lowe, 2004), GLOH (Mikolajczyk and Schmid, 2005), SURF (Bay et al, 2006), P-SURF (Liu et al, 2011), GA-SURF (Wang et al, 2019), PCA-SIFT (Ke and Sukthankar, 2004), VLAD (Jegou et al, 2011), BRISK (Leutenegger et al, 2011), ORB (Rublee et al, 2011), KAZA (Alcantarilla et al, 2012) and AKAZA (Alcantarilla et al, 2013). Xu et al (2016) proposed the mobile visual search guiding system based on SIFT. Many researchers have focused on more compact visual content descriptions (Chandrasekhar et al, 2009;Makar et al, 2009), which are applied to mobile visual search.…”

Section: Mobile Visual Searchmentioning

confidence: 99%

Mobile visual search model for Dunhuang murals in the smart library

Zeng

Sun

et al. 2022

LHT

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PurposeThe purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.Design/methodology/approachFirst, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.FindingsThe findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.Research limitations/implicationsDunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.Originality/valueThe mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.

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Section: Mobile Visual Searchmentioning

confidence: 99%

Mobile visual search model for Dunhuang murals in the smart library

Zeng

Sun

et al. 2022

LHT

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“…Existing MVS research mostly focuses on basic theory (Du et al, 2013;Qi et al, 2017;Lyu et al, 2014), architecture , datasets (Ji et al, 2011), and technical applications (Xu et al, 2016), with little attention paid to MVS usage intention and behavior. noted that although many studies have investigated users' text-based image retrieval behavior, little is known about their content-based image retrieval behavior, especially on mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Consumer Usage of Mobile Visual Search in China

Meng,

Liu,

Huang

et al. 2024

Journal of Global Information Management

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As an emerging information retrieval technology, mobile visual search (MVS) has considerable development potential and broad market application prospects. Although many studies have explored user behaviors with various information technology artifacts, few have specifically investigated consumers' MVS intentions and behaviors. For filling the gap, this study is to integrate perceived contextual offer (PCO) and implementation intention into the theoretical model of UTAUT2, aiming to explore potential predictors of Chinese consumers' MVS usage intention and behavior. The results showed that consumers' perceptions of contextual offer, performance expectancy, hedonic motivation, and their using habit had significant effects on consumers' MVS usage intention. Habit, usage intention, and facilitating conditions showed significant predictive power for consumers' MVS implementation intention and their further usage behavior. Additionally, habit and usage intention indirectly influenced consumers' MVS usage through the complementary mediation of implementation intention.

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The Mobile Visual Search Guiding System Based on SIFT

Abstract: In order to provide personal guiding service to visitors in the Picture Gallery, a mobile visual search guiding system based on SIFT (Scale Invariant Feature Transform)

Cited by 2 publications

References 11 publications

Mobile visual search model for Dunhuang murals in the smart library

Mobile visual search model for Dunhuang murals in the smart library

Consumer Usage of Mobile Visual Search in China

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