Describing and mapping where people experience tranquillity. An exploration based on interviews and Flickr photographs

Peer reviewedPublisher PD

Mapping the visual magnitude of popular tourist sites in Edinburgh city

There is value in being able to automatically measure and visualise the visual magnitude of city sites (monuments and buildings, tourist sites) – for example in urban planning, as an aid to automated way finding, or in augmented reality city guides. Here we present the outputs of an algorithm able to calculate visual magnitude – both as an absolute measure of the façade area, and in terms of a building’s perceived magnitude (its lesser importance with distance). Both metrics influence the photogenic nature of a site. We therefore compared against maps showing the locations from where geo-located FlickR images were taken.  The results accord with the metrics and therefore help disambiguate the meaning  of FlickR tags

The REAL corpus: A crowd-sourced Corpus of human generated and evaluated spatial references to real-world urban scenes

We present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus – Referring Expressions Anchored Language). The REAL corpus contains a collection of images of real-world urban scenes together with verbal descriptions of target objects generated by humans, paired with data on how successful other people were able to identify the same object based on these descriptions. In total, the corpus contains 32 images with on average 27 descriptions per image and 3 verifications for each description. In addition, the corpus is annotated with a variety of linguistically motivated features. The paper highlights issues posed by collecting data using crowd-sourcing with an unrestricted input format, as well as using real-world urban scenes. The corpus will be released via the ELRA repository as part of this submission

Identifying related landmark tags in urban scenes using spatial and semantic clustering

There is considerable interest in developing landmark saliency models as a basis for describing urban landscapes, and in constructing wayfinding instructions, for text and spoken dialogue based systems. The challenge lies in knowing the truthfulness of such models; is what the model considers salient the same as what is perceived by the user? This paper presents a web based experiment in which users were asked to tag and label the most salient features from urban images for the purposes of navigation and exploration. In order to rank landmark popularity in each scene it was necessary to determine which tags related to the same object (e.g. tags relating to a particular café). Existing clustering techniques did not perform well for this task, and it was therefore necessary to develop a new spatial-semantic clustering method which considered the proximity of nearby tags and the similarity of their label content. The annotation similarity was initially calculated using trigrams in conjunction with a synonym list, generating a set of networks formed from the links between related tags. These networks were used to build related word lists encapsulating conceptual connections (e.g. church tower related to clock) so that during a secondary pass of the data related network segments could be merged. This approach gives interesting insight into the partonomic relationships between the constituent parts of landmarks and the range and frequency of terms used to describe them. The knowledge gained from this will be used to help calibrate a landmark saliency model, and to gain a deeper understanding of the terms typically associated with different types of landmarks