Large-scale interactive exploratory visual search

Large scale visual search has been one of the challenging issues in the era of big data. It demands techniques that are not only highly effective and efficient but also allow users conveniently express their information needs and refine their intents. In this thesis, we focus on developing an exploratory framework for large scale visual search. We also develop a number of enabling techniques in this thesis, including compact visual content representation for scalable search, near duplicate video shot detection, and action based event detection. We propose a novel scheme for extremely low bit rate visual search, which sends compressed visual words consisting of vocabulary tree histogram and descriptor orientations rather than descriptors. Compact representation of video data is achieved through identifying keyframes of a video which can also help users comprehend visual content efficiently. We propose a novel Bag-of-Importance model for static video summarization. Near duplicate detection is one of the key issues for large scale visual search, since there exist a large number nearly identical images and videos. We propose an improved near-duplicate video shot detection approach for more effective shot representation. Event detection has been one of the solutions for bridging the semantic gap in visual search. We particular focus on human action centred event detection. We propose an enhanced sparse coding scheme to model human actions. Our proposed approach is able to significantly reduce computational cost while achieving recognition accuracy highly comparable to the state-of-the-art methods. At last, we propose an integrated solution for addressing the prime challenges raised from large-scale interactive visual search. The proposed system is also one of the first attempts for exploratory visual search. It provides users more robust results to satisfy their exploring experiences

Affect-based information retrieval

One of the main challenges Information Retrieval (IR) systems face nowadays originates from the semantic gap problem: the semantic difference between a user’s query representation and the internal representation of an information item in a collection. The gap is further widened when the user is driven by an ill-defined information need, often the result of an anomaly in his/her current state of knowledge. The formulated search queries, which are submitted to the retrieval systems to locate relevant items, produce poor results that do not address the users’ information needs. To deal with information need uncertainty IR systems have employed in the past a range of feedback techniques, which vary from explicit to implicit. The first category of feedback techniques necessitates the communication of explicit relevance judgments, in return for better query reformulations and recommendations of relevant results. However, the latter happens at the expense of users’ cognitive resources and, furthermore, introduces an additional layer of complexity to the search process. On the other hand, implicit feedback techniques make inferences on what is relevant based on observations of user search behaviour. By doing so, they disengage users from the cognitive burden of document rating and relevance assessments. However, both categories of RF techniques determine topical relevance with respect to the cognitive and situational levels of interaction, failing to acknowledge the importance of emotions in cognition and decision making. In this thesis I investigate the role of emotions in the information seeking process and develop affective feedback techniques for interactive IR. This novel feedback framework aims to aid the search process and facilitate a more natural and meaningful interaction. I develop affective models that determine topical relevance based on information gathered from various sensory channels, and enhance their performance using personalisation techniques. Furthermore, I present an operational video retrieval system that employs affective feedback to enrich user profiles and offers meaningful recommendations of unseen videos. The use of affective feedback as a surrogate for the information need is formalised as the Affective Model of Browsing. This is a cognitive model that motivates the use of evidence extracted from the psycho-somatic mobilisation that occurs during cognitive appraisal. Finally, I address some of the ethical and privacy issues that arise from the social-emotional interaction between users and computer systems. This study involves questionnaire data gathered over three user studies, from 74 participants of different educational background, ethnicity and search experience. The results show that affective feedback is a promising area of research and it can improve many aspects of the information seeking process, such as indexing, ranking and recommendation. Eventually, it may be that relevance inferences obtained from affective models will provide a more robust and personalised form of feedback, which will allow us to deal more effectively with issues such as the semantic gap

Using Knowledge Anchors to Facilitate User Exploration of Data Graphs

YesThis paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention