Stefanos Vrochidis

Content-based binary image retrieval using the adaptive hierarchical density histogram

This paper proposes a method for binary image retrieval, where the black-and-white image is represented by a novel feature named the adaptive hierarchical density histogram, which exploits the distribution of the image points on a two-dimensional area. This adaptive hierarchical decomposition technique employs the estimation of point density histograms of image regions, which are determined by a pyramidal grid that is recursively updated through the calculation of image geometric centroids. The extracted descriptor combines global and local properties and can be used in variant types of binary image databases. The validity of the introduced method, which demonstrates high accuracy, low computational cost and scalability, is both theoretically and experimentally shown, while comparison with several other prevailing approaches demonstrates its performance.

Interactive video search tools: a detailed analysis of the video browser showdown 2015

Interactive video retrieval tools developed over the past few years are emerging as powerful alternatives to automatic retrieval approaches by giving the user more control as well as more responsibilities. Current research tries to identify the best combinations of image, audio and text features that combined with innovative UI design maximize the tools performance. We present the last installment of the Video Browser Showdown 2015 which was held in conjunction with the International Conference on MultiMedia Modeling 2015 (MMM 2015) and has the stated aim of pushing for a better integration of the user into the search process. The setup of the competition including the used dataset and the presented tasks as well as the participating tools will be introduced . The performance of those tools will be thoroughly presented and analyzed. Interesting highlights will be marked and some predictions regarding the research focus within the field for the near future will be made.

A hybrid ontology and visual-based retrieval model for cultural heritage multimedia collections

This paper introduces a hybrid multimedia retrieval model that is capable of retrieving cultural heritage multimedia content, based on their semantic annotation with the help of an ontology and on low level visual features with a view to finding similar content. The main novelty is the way in which these techniques cooperate transparently during the evaluation of a single query in a hybrid fashion, making recommendations to the user. A search engine has been developed implementing this model, which is capable of searching through cultural heritage multimedia collections, and indicative examples are discussed, along with insights into its performance.

Building an Environmental Information System for Personalized Content Delivery

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

Discovery of environmental nodes in the web

Analysis and processing of environmental information is considered of utmost importance for humanity. This article addresses the problem of discovery of web resources that provide environmental measurements. Towards the solution of this domain-specific search problem, we combine state-of-the-art search techniques together with advanced textual processing and supervised machine learning. Specifically, we generate domain-specific queries using empirical information and machine learning driven query expansion in order to enhance the initial queries with domain-specific terms. Multiple variations of these queries are submitted to a general-purpose web search engine in order to achieve a high recall performance and we employ a post processing module based on supervised machine learning to improve the precision of the final results. In this work, we focus on the discovery of weather forecast websites and we evaluate our technique by discovering weather nodes for south Finland.

News Articles Classification Using Random Forests and Weighted Multimodal Features

This research investigates the problem of news articles classification. The classification is performed using N-gram textual features extracted from text and visual features generated from one representative image. The application domain is news articles written in English that belong to four categories: Business-Finance, Lifestyle-Leisure, Science-Technology and Sports downloaded from three well-known news web-sites (BBC, Reuters, and TheGuardian). Various classification experiments have been performed with the Random Forests machine learning method using N-gram textual features and visual features from a representative image. Using the N-gram textual features alone led to much better accuracy results (84.4%) than using the visual features alone (53%). However, the use of both N-gram textual features and visual features led to slightly better accuracy results (86.2%). The main contribution of this work is the introduction of a news article classification framework based on Random Forests and multimodal features (textual and visual), as well as the late fusion strategy that makes use of Random Forests operational capabilities.

Environmental data extraction from multimedia resources

Extraction and analysis of environmental information is very important, since it strongly affects everyday life. Nowadays there are already many free services providing environmental information in several formats including multimedia (e.g. map images). Although such presentation formats might be very informative for humans, they complicate the automatic extraction and processing of the underlying data. A characteristic example is the air quality and pollen forecasts, which are usually encoded in image maps, while the initial (numerical) pollutant concentrations remain unavailable. This work proposes a framework for the semi-automatic extraction of such information based on a template configuration tool, on Optical Character Recognition (OCR) techniques and on methodologies for data reconstruction from images. The system is tested with a different air quality and pollen forecast heatmaps demonstrating promising results.

An eye-tracking-based approach to facilitate interactive video search

This paper investigates the role of gaze movements as implicit user feedback during interactive video retrieval tasks. In this context, we use a content-based video search engine to perform an interactive video retrieval experiment, during which, we record the user gaze movements with the aid of an eye-tracking device and generate features for each video shot based on aggregated past user eye fixation and pupil dilation data. Then, we employ support vector machines, in order to train a classifier that could identify shots marked as relevant to a new query topic submitted by new users. The positive results provided by the classifier are used as recommendations for future users, who search for similar topics. The evaluation shows that important information can be extracted from aggregated gaze movements during video retrieval tasks, while the involvement of pupil dilation data improves the performance of the system and facilitates interactive video search.

VERGE: A video interactive retrieval engine

This paper presents the video retrieval engine VERGE, which combines indexing, analysis and retrieval techniques in various modalities (i.e. textual, visual and concept search). The functionalities of the search engine are demonstrated through the supported user interaction modes.

Personalized Environmental Service Orchestration for Quality of Life Improvement

Environmental and meteorological conditions are of utmost importance for the population, as they are strongly related to the quality of life. Citizens are increasingly aware of this importance. This awareness results in an increasing demand for environmental information tailored to their specific needs and background. We present an environmental information platform that supports submission of user queries related to environmental conditions and orchestrates results from complementary services to generate personalized suggestions. From the technical viewpoint, the system discovers and processes reliable data in the web in order to convert them into knowledge. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference. The platform is demonstrated with real world use cases in the south area of Finland showing the impact it can have on the quality of everyday life.