Video retrieval using objects and ostensive relevance feedback

The thesis discusses and evaluates a model of video information retrieval that incorporates a variation of Relevance Feedback and facilitates object-based interaction and ranking. Video and image retrieval systems suffer from poor retrieval performance compared to text-based information retrieval systems and this is mainly due to the poor discrimination power of visual features that provide the search index. Relevance Feedback is an iterative approach where the user provides the system with relevant and non-relevant judgements of the results and the system re-ranks the results based on the user judgements. Relevance feedback for video retrieval can help overcome the poor discrimination power of the features with the user essentially pointing the system in the right direction based on their judgements. The ostensive relevance feedback approach discussed in this work weights user judgements based on the o r d e r in which they are made with newer judgements weighted higher than older judgements. The main aim of the thesis is to explore the benefit of ostensive relevance feedback for video retrieval with a secondary aim of exploring the effectiveness of object retrieval. A user experiment has been developed in which three video retrieval system variants are evaluated on a corpus of video content. The first system applies standard relevance feedback weighting while the second and third apply ostensive relevance feedback with variations in the decay weight. In order to evaluate effective object retrieval, animated video content provides the corpus content for the evaluation experiment as animated content offers the highest performance for object detection and extraction

Fuzzy rule based profiling approach for enterprise information seeking and retrieval

With the exponential growth of information available on the Internet and various organisational intranets there is a need for profile based information seeking and retrieval (IS&R) systems. These systems should be able to support users with their context-aware information needs. This paper presents a new approach for enterprise IS&R systems using fuzzy logic to develop task, user and document profiles to model user information seeking behaviour. Relevance feedback was captured from real users engaged in IS&R tasks. The feedback was used to develop a linear regression model for predicting document relevancy based on implicit relevance indicators. Fuzzy relevance profiles were created using Term Frequency and Inverse Document Frequency (TF/IDF) analysis for the successful user queries. Fuzzy rule based summarisation was used to integrate the three profiles into a unified index reflecting the semantic weight of the query terms related to the task, user and document. The unified index was used to select the most relevant documents and experts related to the query topic. The overall performance of the system was evaluated based on standard precision and recall metrics which show significant improvements in retrieving relevant documents in response to user queries

A novel document ranking algorithm that supports mobile healthcare information access effectiveness

This study presented DROPT; an acronym for Document ranking Optmization algorithm approach, a new idea for the effectiveness of meaningful retrieval results from the information source. Proposed method extracted the frequency of query keyword terms that appears within the user context of Frequently Asked Questions (FAQ) systems on HIV/AIDS content related-documents. The SMS messages were analyzed and then classified, with the aim of constructing a corpus of SMS related to HIV/AIDS. This study presented a novel framework of Information Retrieval Systems (IRS) based on the proposed algorithm. The developed DROPT procedure was used as an evaluation measure. This “Term Frequency-Inverse Document Frequency (TFIDF)” method was applied to obtain the experimental result that was found promising in ranking documents not only the order in which the relevant documents were retrieved, but also both the terms of the relevant documents in feedback and the terms of the irrelevant documents in feedback might be useful for relevance feedback, especially to define its fitness function (mean weight)

Formal concept matching and reinforcement learning in adaptive information retrieval

The superiority of the human brain in information retrieval (IR) tasks seems to come firstly from its ability to read and understand the concepts, ideas or meanings central to documents, in order to reason out the usefulness of documents to information needs, and secondly from its ability to learn from experience and be adaptive to the environment. In this work we attempt to incorporate these properties into the development of an IR model to improve document retrieval. We investigate the applicability of concept lattices, which are based on the theory of Formal Concept Analysis (FCA), to the representation of documents. This allows the use of more elegant representation units, as opposed to keywords, in order to better capture concepts/ideas expressed in natural language text. We also investigate the use of a reinforcement leaming strategy to learn and improve document representations, based on the information present in query statements and user relevance feedback. Features or concepts of each document/query, formulated using FCA, are weighted separately with respect to the documents they are in, and organised into separate concept lattices according to a subsumption relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the concepts in the lattice representation. This avoids implementation drawbacks faced by other FCA-based approaches. Retrieval of a document for an information need is based on concept matching between concept lattice representations of a document and a query. The learning strategy works by making the similarity of relevant documents stronger and non-relevant documents weaker for each query, depending on the relevance judgements of the users on retrieved documents. Our approach is radically different to existing FCA-based approaches in the following respects: concept formulation; weight assignment to object-attribute pairs; the representation of each document in a separate concept lattice; and encoding concept lattices in BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our learning strategy makes use of relevance feedback information to enhance document representations, thus making the document representations dynamic and adaptive to the user interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are presented and compared with published results. In particular, the performance of the system is shown to improve significantly as the system learns from experience.The School of Computing, University of Plymouth, UK

Relevance feedback for best match term weighting algorithms in information retrieval

Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback

A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well

Adaptive image retrieval using a graph model for semantic feature integration

The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)

Combining relevance information in a synchronous collaborative information retrieval environment

Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an information need. Recent advances in both web technologies, such as the sociable web of Web 2.0, and computer hardware, such as tabletop interface devices, have enabled multiple users to collaborate on many computer-related tasks. Due to these advances there is an increasing need to support two or more users searching together at the same time, in order to satisfy a shared information need, which we refer to as Synchronous Collaborative Information Retrieval. Synchronous Collaborative Information Retrieval (SCIR) represents a significant paradigmatic shift from traditional IR systems. In order to support an effective SCIR search, new techniques are required to coordinate users' activities. In this chapter we explore the effectiveness of a sharing of knowledge policy on a collaborating group. Sharing of knowledge refers to the process of passing relevance information across users, if one user finds items of relevance to the search task then the group should benefit in the form of improved ranked lists returned to each searcher. In order to evaluate the proposed techniques we simulate two users searching together through an incremental feedback system. The simulation assumes that users decide on an initial query with which to begin the collaborative search and proceed through the search by providing relevance judgments to the system and receiving a new ranked list. In order to populate these simulations we extract data from the interaction logs of various experimental IR systems from previous Text REtrieval Conference (TREC) workshops

A Four-Factor User Interaction Model for Content-Based Image Retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system