Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Search beyond traditional probabilistic information retrieval

"This thesis focuses on search beyond probabilistic information retrieval. Three ap- proached are proposed beyond the traditional probabilistic modelling. First, term associ- ation is deeply examined. Term association considers the term dependency using a factor analysis based model, instead of treating each term independently. Latent factors, con- sidered the same as the hidden variables of ""eliteness"" introduced by Robertson et al. to gain understanding of the relation among term occurrences and relevance, are measured by the dependencies and occurrences of term sequences and subsequences. Second, an entity-based ranking approach is proposed in an entity system named ""EntityCube"" which has been released by Microsoft for public use. A summarization page is given to summarize the entity information over multiple documents such that the truly relevant entities can be highly possibly searched from multiple documents through integrating the local relevance contributed by proximity and the global enhancer by topic model. Third, multi-source fusion sets up a meta-search engine to combine the ""knowledge"" from different sources. Meta-features, distilled as high-level categories, are deployed to diversify the baselines. Three modified fusion methods are employed, which are re- ciprocal, CombMNZ and CombSUM with three expanded versions. Through extensive experiments on the standard large-scale TREC Genomics data sets, the TREC HARD data sets and the Microsoft EntityCube Web collections, the proposed extended models beyond probabilistic information retrieval show their effectiveness and superiority.

An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents

The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the 201C;noise.201D; Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records

Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM

Source Code Retrieval from Large Software Libraries for Automatic Bug Localization

This dissertation advances the state-of-the-art in information retrieval (IR) based approaches to automatic bug localization in software. In an IR-based approach, one first creates a search engine using a probabilistic or a deterministic model for the files in a software library. Subsequently, a bug report is treated as a query to the search engine for retrieving the files relevant to the bug. With regard to the new work presented, we first demonstrate the importance of taking version histories of the files into account for achieving significant improvements in the precision with which the files related to a bug are located. This is motivated by the realization that the files that have not changed in a long time are likely to have ``stabilized and are therefore less likely to contain bugs. Subsequently, we look at the difficulties created by the fact that developers frequently use abbreviations and concatenations that are not likely to be familiar to someone trying to locate the files related to a bug. We show how an initial query can be automatically reformulated to include the relevant actual terms in the files by an analysis of the files retrieved in response to the original query for terms that are proximal to the original query terms. The last part of this dissertation generalizes our term-proximity based work by using Markov Random Fields (MRF) to model the inter-term dependencies in a query vis-a-vis the files. Our MRF work redresses one of the major defects of the most commonly used modeling approaches in IR, which is the loss of all inter-term relationships in the documents

TOOL SUPPORT FOR CAPTURING THE ESSENCE OF A CONCERN IN SOURCE CODE

Software evolves constantly to adapt to changing user needs. As it evolves, it becomes progressively harder to understand due to accumulation of code changes, increasing code size, and the introduction of complex code dependencies. As a result, it becomes harder to maintain, exposing the software to potential bugs and degradation of code quality. High maintenance costs and diminished opportunities for software reusability and portability lead to reduced return on investment, increasing the likelihood of the software product being discarded or replaced. Nevertheless, we believe that there is value in legacy software due to the amount of intellectual efforts that have been invested in it. To extend its value, we utilize the common practice of identifying the pieces of code relevant to a given concern. Identifying relevant code is a manual process and relies on domain and code expertise. This makes it difficult to scale to large and complex code. In this thesis, we propose several automated approaches for capturing the essential code that represents a concern of interest. We utilize dynamic program analysis of execution traces to identify a relevant code subset. Information retrieval techniques are then utilized to improve the accuracy of the capture, refine the process, and verify the results