Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

Chest radiography doses with film screen : is further reduction possible?

Peer reviewedPublisher PD

An image retrieval system based on explicit and implicit feedback on a tablet computer

Our research aims at developing a image retrieval system which uses relevance feedback to build a hybrid search /recommendation system for images according to users’ inter ests. An image retrieval application running on a tablet computer gathers explicit feedback through the touchscreen but also uses multiple sensing technologies to gather implicit feedback such as emotion and action. A recommendation mechanism driven by collaborative filtering is implemented to verify our interaction design

A study into annotation ranking metrics in geo-tagged image corpora

Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task

Context-aware person identification in personal photo collections

Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone

K-Space at TRECVid 2008

In this paper we describe K-Space’s participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde & Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results