A Probabilistic Framework for Information Modelling and Retrieval Based on User Annotations on Digital Objects

Annotations are a means to make critical remarks, to explain and comment things, to add notes and give opinions, and to relate objects. Nowadays, they can be found in digital libraries and collaboratories, for example as a building block for scientific discussion on the one hand or as private notes on the other. We further find them in product reviews, scientific databases and many "Web 2.0" applications; even well-established concepts like emails can be regarded as annotations in a certain sense. Digital annotations can be (textual) comments, markings (i.e. highlighted parts) and references to other documents or document parts. Since annotations convey information which is potentially important to satisfy a user's information need, this thesis tries to answer the question of how to exploit annotations for information retrieval. It gives a first answer to the question if retrieval effectiveness can be improved with annotations. A survey of the "annotation universe" reveals some facets of annotations; for example, they can be content level annotations (extending the content of the annotation object) or meta level ones (saying something about the annotated object). Besides the annotations themselves, other objects created during the process of annotation can be interesting for retrieval, these being the annotated fragments. These objects are integrated into an object-oriented model comprising digital objects such as structured documents and annotations as well as fragments. In this model, the different relationships among the various objects are reflected. From this model, the basic data structure for annotation-based retrieval, the structured annotation hypertext, is derived. In order to thoroughly exploit the information contained in structured annotation hypertexts, a probabilistic, object-oriented logical framework called POLAR is introduced. In POLAR, structured annotation hypertexts can be modelled by means of probabilistic propositions and four-valued logics. POLAR allows for specifying several relationships among annotations and annotated (sub)parts or fragments. Queries can be posed to extract the knowledge contained in structured annotation hypertexts. POLAR supports annotation-based retrieval, i.e. document and discussion search, by applying an augmentation strategy (knowledge augmentation, propagating propositions from subcontexts like annotations, or relevance augmentation, where retrieval status values are propagated) in conjunction with probabilistic inference, where P(d -> q), the probability that a document d implies a query q, is estimated. POLAR's semantics is based on possible worlds and accessibility relations. It is implemented on top of four-valued probabilistic Datalog. POLAR's core retrieval functionality, knowledge augmentation with probabilistic inference, is evaluated for discussion and document search. The experiments show that all relevant POLAR objects, merged annotation targets, fragments and content annotations, are able to increase retrieval effectiveness when used as a context for discussion or document search. Additional experiments reveal that we can determine the polarity of annotations with an accuracy of around 80%

An MPEG-7 scheme for semantic content modelling and filtering of digital video

Abstract Part 5 of the MPEG-7 standard specifies Multimedia Description Schemes (MDS); that is, the format multimedia content models should conform to in order to ensure interoperability across multiple platforms and applications. However, the standard does not specify how the content or the associated model may be filtered. This paper proposes an MPEG-7 scheme which can be deployed for digital video content modelling and filtering. The proposed scheme, COSMOS-7, produces rich and multi-faceted semantic content models and supports a content-based filtering approach that only analyses content relating directly to the preferred content requirements of the user. We present details of the scheme, front-end systems used for content modelling and filtering and experiences with a number of users

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

Abductive retrieval for multimedia information seeking

In this paper we discuss an approach to the retrieval of data annotated using the MPEG-7 multimedia description schema. In particular we describe a framework for the retrieval of annotated video samples that is based on principles from the area of abductive reasoning

Towards a geometrical model for polyrepresentation of information objects

The principle of polyrepresentation is one of the fundamental recent developments in the field of interactive retrieval. An open problem is how to define a framework which unifies different as- pects of polyrepresentation and allows for their application in several ways. Such a framework can be of geometrical nature and it may embrace concepts known from quantum theory. In this short paper, we discuss by giving examples how this framework can look like, with a focus on in- formation objects. We further show how it can be exploited to find a cognitive overlap of different representations on the one hand, and to combine different representations by means of knowledge augmentation on the other hand. We discuss the potential that lies within a geometrical frame- work and motivate its further developmen

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

Annotation Search: the FAST Way

Περιέχει το πλήρες κείμενοThis paper discusses how annotations can be exploited to develop information access and retrieval algorithms that take them into account. The paper proposes a general framework for developing such algorithms that specifically deals with the problem of accessing and retrieving topical information from annotations and annotated documents

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