A Survey on Mobile Landmark Recognition for Information Retrieval

Chen, Tao; Wu, Kui; Yap, Kim–Hui; Li, Zhen; Tsai, Flora S.

doi:10.1109/mdm.2009.107

Cited by 30 publications

(15 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The interest towards this task grew together with the number of freely available images on the Internet, many of which are geo-tagged and depict urban outdoor scenes. Today, with the widespread use of mobile devices endowed with builtin cameras and Internet connectivity, location recognition is a useful tool for city guides and smart navigation aids that are able to localize an image in near real time [8,9].…”

Section: Related Workmentioning

confidence: 99%

Location recognition over large time lags

Fernando

Tommasi

Tuytelaars

2015

Computer Vision and Image Understanding

View full text Add to dashboard Cite

Would it be possible to automatically associate ancient pictures to modern ones and create fancy cultural heritage city maps? We introduce here the task of recognizing the location depicted in an old photo given modern annotated images collected from the Internet. We present an extensive analysis on different features, looking for the most discriminative and most robust to the image variability induced by large time lags. Moreover, we show that the described task benefits from domain adaptation.

show abstract

Section: Related Workmentioning

confidence: 99%

Location recognition over large time lags

Fernando

Tommasi

Tuytelaars

2015

Computer Vision and Image Understanding

View full text Add to dashboard Cite

show abstract

“…The interest in monument and landmark recognition comes before the latest advancements in DL. Chen et al (2009) and Amato et al (2015) present reviews of such applications of image recognition techniques. Since 2012, CNNs started outperforming previously used algorithms in detecting and classifying objects from images (Amato et al, 2016).…”

Section: Deep Learning and Monument Recognitionmentioning

confidence: 99%

Towards Deep Learning for Architecture: A Monument Recognition Mobile App

Palma

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines. This paper introduces a project aimed at studying CNN techniques in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed. While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.</p>

show abstract

“…In Chen et al [2009], a survey of the literature on mobile landmark recognition for information retrieval is given. The classification methods reported include SVM, Adaboost, the Bayesian model, HMM, and GMM.…”

Section: Related Workmentioning

confidence: 99%

Fast Image Classification for Monument Recognition

Amato

Falchi

Gennaro

2015

J. Comput. Cult. Herit.

View full text Add to dashboard Cite

Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the k-nearest neighbor (kNN) is one of the most simple and widely used methods. In this article, we use kNN classification and landmark recognition techniques to address the problem of monument recognition in images. We propose two novel approaches that exploit kNN classification technique in conjunction with local visual descriptors.The first approach is based on a relaxed definition of the local feature based image to image similarity and allows standard kNN classification to be efficiently executed with the support of access methods for similarity search.The second approach uses kNN classification to classify local features rather than images. An image is classified evaluating the consensus among the classification of its local features. In this case, access methods for similarity search can be used to make the classification approach efficient.The proposed strategies were extensively tested and compared against other state-of-the-art alternatives in a monument and cultural heritage landmark recognition setting. The results proved the superiority of our approaches.An additional relevant contribution of this work is the exhaustive comparison of various types of local features and image matching solutions for recognition of monuments and cultural heritage related landmarks.

show abstract

A Survey on Mobile Landmark Recognition for Information Retrieval

Cited by 30 publications

References 29 publications

Location recognition over large time lags

Location recognition over large time lags

Towards Deep Learning for Architecture: A Monument Recognition Mobile App

Fast Image Classification for Monument Recognition

Contact Info

Product

Resources

About