A survey on context-aware mobile visual recognition

Min, Weiqing; Jiang, Shuqiang; Wang, Shuhui; Xu, Ruihan; Cao, Yushan; Herranz, Luis; He, Zhiqiang

doi:10.1007/s00530-016-0523-8

Cited by 10 publications

(3 citation statements)

References 97 publications

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“…The computer vision and multimedia research communities have extensively explored the use of geotagged images for different applications [18,23], we discuss here the most related to our work. However, to the best of our knowledge, the task of location sensitive retrieval as defined before, has not yet been addressed.…”

Section: Related Workmentioning

confidence: 99%

Location Sensitive Image Retrieval and Tagging

Moreno¹,

Gibert²,

Gómez³

et al. 2020

Preprint

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People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.

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Section: Related Workmentioning

confidence: 99%

Location Sensitive Image Retrieval and Tagging

Moreno¹,

Gibert²,

Gómez³

et al. 2020

Preprint

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show abstract

“…The location service formed by the integration of navigation and positioning, mobile interconnection, cloud computing, and other technologies is applied to the tourism industry (smart tourism), which will significantly improve the level of tourist services, refined management, and emergency handling in scenic spots [2]. Since mobile smart devices need to locate themselves before use to further perform path planning based on the data collected by sensors and other tasks that require high positioning accuracy, the research needs for spatial positioning technology in smart device applications are also increasing [3].…”

Section: Introductionmentioning

confidence: 99%

Tourist Street View Navigation and Tourist Positioning Based on Multimodal Wireless Virtual Reality

2021

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Based on the multimodal wireless virtual reality technology, this article researches and implements two different spatial positioning estimation systems based on intelligent networks and mixed reality devices, which can estimate their position and attitude in space with higher accuracy. Based on the multimodal protocol, an extensible information exchange protocol is designed to meet the requirements of the terminal and the background service in the scenic spot, which realizes the information interaction between the terminal and the background service and supports the realization of system functions. Second, a real-time data management module is designed for the two types of terminal historical location query and real-time location query. Through the research and summary of GPS positioning technology and existing matching algorithms, as well as the analysis of positioning errors, a scenic map matching algorithm based on an improved virtual reality model is proposed. Later, in the electronic map design, the traditional linear street view model was improved, the width element of the street view was increased, and the rectangular street view model was introduced. When the candidate road section was selected, the candidate road section was determined by the area intersection method of the error rectangle and the rectangular street view, and the search path was improved, while reducing omissions in the selection of candidate road sections. In the process of adaptive moving target feature learning, it can effectively remove the inaccurate foreground extraction caused by the misjudgement and misdetection of the multimodal background modeling method. This plays an important role in the later trajectory tracking and reduces the uninterruptedness. The simulation results show that the real-time data is cached in the memory, and the real-time data is divided and organized on the basis of region, which realizes efficient designated terminal location query, range query, neighbour query, and many other typical real-time location-based queries. Finally, using the characteristics of the database management system adopted, through a reasonable design of the database table structure, efficient access to historical data is realized. System test results show that the system has realized multimedia applications, electronic maps, travel notes, and other functions and has a friendly interface, easy operation, and good maintainability and scalability.

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“…Furthermore, food recommendation should consider more context information. Besides basic context information captured from familiar mobile devices [5], such as time, location and environmental information (e.g., temperature and PM2.5), various body state-related signals, such as steps taken, heart rate, sleep quality, body acceleration and even affective states can also be captured from new sensing devices, such as watches, wearable fitness trackers and bracelets [6]. These signals can describe users' actual body conditions comprehensively in real-time and is of crucial importance for food recommendation.…”

Section: Introductionmentioning

confidence: 99%

Food Recommendation: Framework, Existing Solutions, and Challenges

Min

Jiang

Jain

2020

IEEE Trans. Multimedia

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A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field.

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A survey on context-aware mobile visual recognition

Cited by 10 publications

References 97 publications

Location Sensitive Image Retrieval and Tagging

Location Sensitive Image Retrieval and Tagging

Tourist Street View Navigation and Tourist Positioning Based on Multimodal Wireless Virtual Reality

Food Recommendation: Framework, Existing Solutions, and Challenges

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