A Mobile Vision System for Urban Detection with Informative Local Descriptors

Fritz, Gerald; Seifert, Christin; Paletta, Lucas

doi:10.1109/icvs.2006.5

Cited by 82 publications

(67 citation statements)

References 14 publications

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“…For classification, however, they apply an adapted text retrieval technique. Nonetheless, the recognition is comparable to that of [12]. Bay et al [2] introduced a museum guide based on a tablet PC.…”

Section: Museum Guidance Systemsmentioning

confidence: 56%

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Subobject detection through spatial relationships on mobile phones

Brombach

Bruns

Bimber

2009

Proceedings of the 14th International Conference on Intelligent User Interfaces

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We present a novel image classification technique for detecting multiple objects (called subobjects) in a single image. In addition to image classifiers, we apply spatial relationships among the subobjects to verify and to predict locations of detected and undetected subobjects, respectively. By continuously refining the spatial relationships throughout the detection process, even locations of completely occluded exhibits can be determined. This approach is applied in the context of PhoneGuide, an adaptive museum guidance system for camera-equipped mobile phones. Laboratory tests as well as a field experiment reveal recognition rates and performance improvements when compared to related approaches.

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Section: Museum Guidance Systemsmentioning

confidence: 56%

“…Fritz et al [12] introduced a city guide for mobile phones: Datasets including photographs of buildings or monuments and the respective GPS information are captured by tourists and transferred to a remote server via UMTS or GPRS. On the server, the images are compared with a database of known sights via SIFT classification [15].…”

Section: Museum Guidance Systemsmentioning

confidence: 99%

Subobject detection through spatial relationships on mobile phones

Brombach

Bruns

Bimber

2009

Proceedings of the 14th International Conference on Intelligent User Interfaces

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“…This very efficient yet powerful approach have been used by many authors for different tasks such as the localization of the camera [5,10], the reconstruction of 3D scenes by assembling images searched over the Internet [5,10] or the navigation of autonomous robots [22].…”

Section: Query-by-example Image Searchmentioning

confidence: 99%

Learning Distance Functions for Automatic Annotation of Images

Krapac

Jurie

2008

Lecture Notes in Computer Science

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Abstract. This paper gives an overview of recent approaches towards image representation and image similarity computation for content-based image retrieval and automatic image annotation (category tagging). Additionaly, a new similarity function between an image and an object class is proposed. This similarity function combines various aspects of object class appearance through use of representative images of the class. Similarity to a representative image is determined by weighting local image similarities, where weights are learned from training image pairs, labeled "same" and "different", using linear SVM. The proposed approach is validated on a challenging dataset where it performed favorably.

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“…In their system a user can take a picture of one of 120 landmarks located around Singapore (STOIC dataset), which is then identified on a remote server. Fritz et al [7] use SIFT features for descriptor matching in a mobile landmark recognition system. They use a relatively small dataset of 1005 landmark images (ZUBUD dataset) based around Zurich as their training collection.…”

Section: Introductionmentioning

confidence: 99%

Automated Annotation of Landmark Images Using Community Contributed Datasets and Web Resources

Jones

Byrne

Hughes

et al. 2011

Semantic Multimedia

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Abstract.A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching offers 93.6% classification accuracy; the selection of appropriate tags for use in annotation performs well (F1M of 0.59), and it subsequently automatically identifies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases.

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A Mobile Vision System for Urban Detection with Informative Local Descriptors

Abstract: We present a computer vision system for the detection

Cited by 82 publications

References 14 publications

Subobject detection through spatial relationships on mobile phones

Subobject detection through spatial relationships on mobile phones

Learning Distance Functions for Automatic Annotation of Images

Automated Annotation of Landmark Images Using Community Contributed Datasets and Web Resources

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