Το διαδίκτυο στην Κύπρο 2010, Τελική Έκθεση

Για την αναπαραγωγή αυτής της έκθεσης σε κάθε άλλη μορφή πέραν της χρήσης συνοπτικών αποσπασμάτων απαιτείται ρητή γραπτή άδεια από το World Internet Project Cyprus.Χρηματοδοτούμενη από το ΤΕΠΑΚ, το δεύτερο κύμα της έρευνας «The Cyprus World Internet Project» διεξάχθηκε κατά το διάστημα Μάιος- Ιούνιος 2010 μέσω προσωπικών συνεντεύξεων ενός δείγματος 1000 ατόμων από την Ελληνοκυπριακή και 600 ατόμων από την Τουρκοκυπριακή κοινότητα. Το πρώτο κύμα της έρευνας πραγματοποιήθηκε το 2008 και αφορούσε μόνο τους Ελληνοκύπριους.Τεχνολογικό Πανεπιστήμιο Κύπρο

Broadcast news parsing using visual cues: a robust face detection approach

Automatic content-based analysis and indexing of broadcast news recordings or digitized news archives is becoming an important tool in the framework of many multimedia interactive services such as news summarization, browsing, retrieval and news-on-demand (NoD) applications. Existing approaches have achieved high performance in such applications but heavily rely on textual cues such as closed caption tokens and teletext transcripts. We present an efficient technique for temporal segmentation and parsing of news recordings based on visual cues that can either be employed as a stand-alone application for non-closed captioned broadcasts or integrated with audio and textual cues of existing systems. The technique involves robust face detection by means of color segmentation, skin color matching and shape processing, and is able to identify typical news instances like anchor persons, reports and outdoor shot

Web Image Indexing Using WICE and a Learning-Free Language Model

Part 3: Ontology-Web and Social Media AI Modeling (OWESOM)International audienceWith the advent of Web 2.0 and the rapidly increasing popularity of online social networks that make extended use of visual information, like Facebook and Instagram, web image indexing regained great attention among the researchers in the areas of image indexing and information retrieval. Web image indexing is traditionally approached, by commercial search engines, using text-based information such as image file names, anchor text, web-page keywords and, of course, surrounding text. In the latter case, for effective indexing, two requirements should be met: Correct identification of the related text, known as image context, and extraction of the right terms from this text. Usually, researchers working in the field of web image indexing consider that once the image context is identified extraction of indexing terms is trivial. However, we have shown in our previous work that this is not the rule of thumb.In this paper we get advantage of Web Image Context Extraction (WICE) using visual web-page parsing and specific distance metrics and following this we locate key terms within this text to index the image using language models. In this way, the proposed method is totally learning free, i.e., no corpus need to be collected to train the keyword extraction component, while the identified indexing terms are more descriptive for the image since they are extracted from a portion of web-page’s text. This deviates from the traditional web image indexing approach in which keywords are extracted from all text in the web-page. The evaluation, performed on a dataset of 978 manually annotated web images taken from 243 web pages, shows the effectiveness of the proposed approach both in image context extraction and indexing

A Hierarchical Classification Scheme for Semantic Image Annotation

In this paper we address some of the issues commonly encountered in automatic image annotation systems such as simultaneous labeling with keywords corresponding to both abstract terms and object classes, multiple keyword assignment, and low accuracy of labeling due to concurrent categorization to multiple classes. We propose a hierarchical classification scheme which is based on predefined XML-dictionaries of tree form. Every node of such a tree defines a particular classification task while the children of the node correspond to classification categories. The winning class (subnode) defines the subsequent classification task and the process continues until the leafs of the tree are reached. The final classification task is performed at image segment level; that is every image segment is assigned a particular keyword corresponding to a tree leaf. The path followed from the root of the XML tree to the leafs along with the union of labels assigned to the image segments compose the list of annotation keywords for the input image. The performance of the proposed method was tested on a set of 1046 images, taken from the athletics domain, containing a total of 3546 concept instances of 33 different concepts. The results promising and show the potential of the divide and conquer approach we follow through the proposed hierarchical classification schem

Image retrieval via topic modelling of Instagram hashtags

Automatic Image Annotation (AIA) is the process of assigning tags to digital images without the intervention of humans. Most of the modern automatic image annotation methods are based on the learning by example paradigm. In those methods building the training examples, that is, pairs of images and related tags, is the first critical step. We have shown in our previous studies that hashtags accompanying images in social media and especially the Instagram provide a reach source for creating training sets for AIA. However, we concluded that only 20% of the Instagram hashtags describe the actual content of the image they accompany, thus, a series of filtering steps need to apply in order to identify the appropriate hashtags. In this paper we apply graph based topic modelling on Instagram hashtags in order to predict the subject of the related images and we propose an innovativeimage retrieval scheme that can be used in the context of Instagram with minimal training requirements

Proceedings - 2010 5th International Workshop on Semantic Media Adaptation and Personalization, SMAP 2010: Preface

Proceedings - 2010 5th International Workshop on Semantic Media Adaptation and Personalization, SMAP 2010 2010, Article number 5706858, Pages iii-i

Classification of Instagram photos: Topic modelling vs transfer learning

The existence of pre-trained deep learning models for image classification, such as those trained on the well-known Resnet-50 architecture, allows for easy application of transfer learning to several domains including image retrieval. Recently, we proposed topic modelling for the retrieval of Instagram photos based on the associated hashtags. In this paper we compare content-based image classification, based on transfer learning, with the classification based on topic modelling of Instagram hashtags for a set of 24 different concepts. The comparison was performed on a set of 1944 Instagram photos, 81 per concept. Despite the excellent performance of the pre-trained deep learning models, it appears that text-based retrieval, as performed by the topic models of Instagram hashtags, stills perform better