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

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

Multimedia Markup Tools for OpenKnowledge

OpenKnowledge is a peer-to-peer system for sharing knowledge and is driven by interaction models that give the necessary context for mapping of ontological knowledge fragments necessary for the interaction to take place. The OpenKnowledge system is agnostic to any specific data formats that are used in the interactions, relying on ontology mapping techniques for shimming the messages. The potentially large search space for matching ontologies is reduced by the shared context of the interaction. In this paper we investigate what this means for multimedia data on the OpenKnowledge network by discussing how an existing application that provides multimedia annotation (the Semantic Logger) can be migrated into the OpenKnowledge domain

Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches

Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

How MPEG Query Format enables advanced multimedia functionalities

In December 2008, ISO/IEC SC29WG11 (more commonly known as MPEG) published the ISO/IEC 15938-12 standard, i.e. the MPEG Query Format (MPQF), providing a uniform search&retrieval interface for multimedia repositories. While the MPQF’s coverage of basic retrieval functionalities is unequivocal, it’s suitability for advanced retrieval tasks is still under discussion. This paper analyzes how MPQF addresses four of the most relevant approaches for advanced multimedia retrieval: Query By Example (QBE), Retrieval trough Semantic Indexing, Interactive Retrieval, and Personalized and Adaptive Retrieval. The paper analyzes the contribution of MPQF in narrowing the semantic gap, and the flexibility of the standard. The paper proposes several language extensions to solve the different identified limitations. These extensions are intended to contribute to the forthcoming standardization process of the envisaged MPQF’s version 2.Postprint (author’s final draft

NinSuna: a fully integrated platform for format-independent multimedia content adaptation and delivery using Semantic Web technologies

The current multimedia landscape is characterized by a significant heterogeneity in terms of coding and delivery formats, usage environments, and user preferences. The main contribution of this paper is a discussion of the design and functioning of a fully integrated platform for multimedia adaptation and delivery, called NinSuna. This platform is able to efficiently deal with the aforementioned heterogeneity in the present-day multimedia ecosystem, thanks to the use of format-agnostic adaptation engines (i.e., engines independent of the underlying coding format) and format-agnostic packaging engines (i.e., engines independent of the underlying delivery format). Moreover, NinSuna also provides a seamless integration between metadata standards and adaptation processes. Both our format-independent adaptation and packaging techniques rely on a model for multimedia bitstreams, describing the structural, semantic, and scalability properties of these multimedia streams. News sequences were used as a test case for our platform, enabling the user to select news fragments matching his/her specific interests and usage environment characteristics

COSMOS-7: Video-oriented MPEG-7 scheme for modelling and filtering of semantic content

MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves it open as to how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effort of the user. This paper proposes an MPEG-7 scheme that can be deployed for semantic content modelling and filtering of digital video. 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