Christos Zigkolis

Cluster-Based Landmark and Event Detection for Tagged Photo Collections

An image analysis scheme can automate the detection of landmarks and events in large image collections, significantly improving the content-consumption experience.

Image clustering through community detection on hybrid image similarity graphs

The wide adoption of photo sharing applications such as Flickr

ClustTour: city exploration by use of hybrid photo clustering

We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of photo clusters corresponding to landmarks and events. Our application, called ClustTour, is based on an efficient landmark and event detection scheme for tagged photo collections. The proposed scheme relies on the combination of a graph-based photo clustering algorithm, making use of both visual and tag information of photos, with a cluster classification and merging module. ClustTour creates a map-based visualization of the identified photo clusters that are classified in prominent categories and are filterable by time and tag. We believe that such an application can greatly facilitate the task of knowing a city through its landmarks and events. So far, the demo has been based on a large photo dataset focused on Barcelona, and it is gradually expanding to contain photo clusters of several major cities of Europe. Furthermore, an Android application is developed that complements the web-based version of ClustTour.

Collaborative event annotation in tagged photo collections

Events constitute a significant means of multimedia content organization and sharing. Despite the recent interest in detecting events and annotating media content in an event-centric way, there is currently insufficient support for managing events in large-scale content collections and limited understanding of the event annotation process. To this end, this paper presents CrEve, a collaborative event annotation framework which uses content found in social media sites with the prime objective to facilitate the annotation of large media corpora with event information. The proposed annotation framework could significantly benefit social media research due to the proliferation of event-related user-contributed content. We demonstrate that, compared to a standard “browse-and-annotate” interface, CrEve leads to a 19% increase in the coverage of the generated ground truth in a large-scale annotation experiment. Furthermore, the paper discusses the results of a user study that quantifies the performance of CrEve and the contribution of different event dimensions in the event annotation process. The study confirms the prevalence of spatio-temporal queries as the prime option of discovering event-related content in a large collection. In addition, textual queries and social cues (content contributor) were also found to be significant as event search dimensions. Finally, it demonstrates the potential of employing automatic photo clustering methods with the goal of facilitating event annotation.

City exploration by use of spatio-temporal analysis and clustering of user contributed photos

We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of spatio-temporal analysis and clustering of user contributed photos. Our framework analyzes the spatial distribution of large city-centered collections of user contributed photos at different time scales in order to index the most popular spots of a city in a time-aware manner. Subsequently, the photo sets belonging to the same spatiotemporal context are clustered in order to extract representative photos for each spot. The resulting application enables users to obtain flexible summaries of the most important spots in a city given a temporal slice (time of the day, month, season). The demonstration will be based on a photo dataset covering major European cities.

Semi-supervised object recognition using flickr images

In this work we present an algorithm for extracting region level annotations from flickr images using a small set of manually labelled regions to guide the selection process. More specifically, we construct a set of flickr images that focuses on a certain concept and apply a novel graph based clustering algorithm on their regions. Then, we select the cluster or clusters that correspond to the examined concept guided by the manually labelled data. Experimental results show that although the obtained regions are of lower quality compared to the manually labelled regions, the gain in effort compensates for the loss in performance.

Detecting the long-tail of Points of Interest in tagged photo collections

The paper tackles the problem of matching the photos of a tagged photo collection to a list of “long-tail” Points Of Interest (PoIs), that is PoIs that are not very popular and thus not well represented in the photo collection. Despite the significance of improving “long-tail” PoI photo retrieval for travel applications, most landmark detection methods to date have been tested on very popular landmarks. In this paper, we conduct a thorough empirical analysis comparing four baseline matching methods that rely on photo metadata, three variants of an approach that uses cluster analysis in order to discover PoI-related photo clusters, and a real-world retrieval mechanism (Flickr search) on a set of less popular PoIs. A user-based evaluation of the aforementioned methods is conducted on a Flickr photo collection of over 100, 000 photos from 10 well-known touristic destinations in Greece. A set of 104 “long-tail” PoIs is collected for these destinations from Wikipedia, Wikimapia and OpenStreetMap. The results demonstrate that two of the baseline methods outperform Flickr search in terms of precision and F-measure, whereas two of the cluster-based methods outperform it in terms of recall and PoI coverage. We consider the results of this study valuable for enhancing the indexing of pictorial content in social media sites.

A comparative study of spatial, temporal and content-based patterns emerging in YouTube and Flickr

Due to the recent advances and wide adoption of Web 2.0 technologies, there is an abundance of publicly available user generated content, which can be a valuable resource for researchers, enabling them to apply sophisticated analysis methods on data of unprecedented scale. analysis methods on data of unprecedented scale.

Integrating similarity and dissimilarity notions in recommenders

Abstract Collaborative recommenders rely on the assumption that similar users may exhibit similar tastes while content-based ones favour items that found to be similar with the items a user likes. Weak related entities, which are often considered to be useful, are neglected by those similarity-driven recommenders. To take advantage of this neglected information, we introduce a novel dissimilarity-based recommender that bases its estimations on degrees of dissimilarities among items’ attributes. However, instead of using the proposed recommender as a stand-alone method, we combine it with similarity-based ones to maintain the selective nature of the latter while detecting, through our recommender, information that may have been overlooked. Such combinations are established by IANOS, a proposed framework through which we increase the accuracy of two popular similarity-based recommenders (Naive Bayes and Slope-One) after their combination with our algorithm. Improved accuracy results in experimentation on two datasets (Yahoo! Movies and Movielens) enhance our reasoning. However, the proposed recommender comes with an additional computational complexity when combined with other techniques. By using Hadoop technology, we developed a distributed version of IANOS through which execution time was reduced. Evaluation on IANOS procedures in terms of time performance endorses the use of distributed implementations.

Dynamic Code Generation for Cultural Content Management

Digital repositories are popular means for preserving, restoring, and indexing archaeological and cultural content. They provide the base for development of a fauna of related applications including virtual tours and data management. Common difficulties such as the ever changing software specifications from domain experts make this task challenging as the alterations of the database schema lead to massive code rewrites. Within this context we propose and implement in practice a model for automated code generation building essentially a content management application by traversing a custom tree-based ERschema.