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 and 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

Multimodal Data Analytics and Fusion for Data Science

Advances in technologies have rapidly accumulated a zettabyte of “new” data every two years. The huge amount of data have a powerful impact on various areas in science and engineering and generates enormous research opportunities, which calls for the design and development of advanced approaches in data analytics. Given such demands, data science has become an emerging hot topic in both industry and academia, ranging from basic business solutions, technological innovations, and multidisciplinary research to political decisions, urban planning, and policymaking. Within the scope of this dissertation, a multimodal data analytics and fusion framework is proposed for data-driven knowledge discovery and cross-modality semantic concept detection. The proposed framework can explore useful knowledge hidden in different formats of data and incorporate representation learning from data in multimodalities, especial for disaster information management. First, a Feature Affinity-based Multiple Correspondence Analysis (FA-MCA) method is presented to analyze the correlations between low-level features from different features, and an MCA-based Neural Network (MCA-NN) ispro- posedto capture the high-level features from individual FA-MCA models and seamlessly integrate the semantic data representations for video concept detection. Next, a genetic algorithm-based approach is presented for deep neural network selection. Furthermore, the improved genetic algorithm is integrated with deep neural networks to generate populations for producing optimal deep representation learning models. Then, the multimodal deep representation learning framework is proposed to incorporate the semantic representations from data in multiple modalities efficiently. At last, fusion strategies are applied to accommodate multiple modalities. In this framework, cross-modal mapping strategies are also proposed to organize the features in a better structure to improve the overall performance