Integrating Image Segmentation and Classification for Fuzzy Knowledge-Based Multimedia Indexing (original) (raw)
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A Fuzzy Ontology – Based Framework for Reasoning in Visual Video Content Analysis and Indexing
Multimedia indexing systems based on semantic concept detectors are incomplete in the semantic sense. We can improve the effectiveness of these systems by using knowledge-based approaches which utilize semantic knowledge. In this paper, we propose a novel and efficient approach to enhance semantic concept detection in multimedia content, by exploiting contextual information about concepts from visual modality. First, a semantic knowledge is extracted via a contextual annotation framework. Second, a Fuzzy ontology is proposed to represent the fuzzy relationships (roles and rules) among every context and its semantic concepts. We use an abduction engine based on βeta function as a membership function for fuzzy rules. Third, a deduction engine is used to handle richer results in our video indexing system by running the proposed fuzzy ontology. Experiments on TRECVID 2010 benchmark have been performed to evaluate the performance of this approach. The obtained results show consistent improvement in semantic concepts detection, when a context space is used, and a good degree of indexing effectiveness as compared to existing approaches.
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The task of multimedia document categorization forms a well-known problem in information retrieval. The task is to assign a multimedia document to one or more categories, based on its contents. In this case, effective management and thematic categorization requires usually the extraction of the underlying semantics. The proposed approach utilizes as input, analyzes and exploits the textual annotation that accompanies a multimedia document, in order to extract its underlying semantics, construct a semantic index and finally classify the documents to thematic categories. This process is based on a unified knowledge and semantics representation model introduced, as well as basic principles of fuzzy relational algebra. On top of that the fuzzy extension of expressive description logic language SHIN , f-SHIN and its reasoning services are used to further refine and optimize the initial categorization results. The proposed approach was tested on a set of real-life multimedia documents, derived from the Internet 1 , as well as personal databases and shows rather promising results.
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In this paper we discuss the use of knowledge for the analysis and semantic retrieval of video. We follow a fuzzy relational approach to knowledge representation, based on which we define and extract the context of either a multimedia document or a user query. During indexing, the context of the document is utilized for the detection of objects and for automatic thematic categorization.
Semantic Representation of Multimedia Content - Knowledge Representation and Semantic Indexing
In this paper we present a framework for unified, personalized access to heterogeneous multimedia content in distributed repositories. Focusing on semantic analysis of multimedia documents, metadata, user queries and user profiles, it contributes to the bridging of the gap between the semantic nature of user queries and raw multimedia documents. The proposed approach utilizes as input visual content analysis results, as well as analyzes and exploits associated textual annotation, in order to extract the underlying semantics, construct a semantic index and classify documents to topics, based on a unified knowledge and semantics representation model. It may then accept user queries, and, carrying out semantic interpretation and expansion, retrieve documents from the index and rank them according to user preferences, similarly to text retrieval. All processes are based on a novel semantic processing methodology, employing fuzzy algebra and principles of taxonomic knowledge representation. Part I of this work presented in this paper deals with data and knowledge models, manipulation of multimedia content annotations and semantic indexing, while Part II will continue on the use of the extracted semantic information for personalized retrieval.
Fifth International Symposium on Multimedia Software Engineering, 2003. Proceedings., 2003
To automatically index image databases in a semantic way is very challenging. This is because currently only low level features of images, such as colour, texture, and shape can be extracted automatically by computers, but humans recognise images based on high level concepts. This difference between current machine operation and human indexing is known as the semantic gap problem of content-based image retrieval (CBIR) systems. The semantic gap problem leads to users often finding the results produced by CBIR systems to be unsatisfactory.
Soft Computing in …, 2006
The semantic gap is the main problem of content based multimedia retrieval. This refers to the extraction of the semantic content of multimedia documents, the understanding of user information needs and requests, as well as to the matching between the two. In this chapter we focus on the analysis of multimedia documents for the extraction of their semantic content. Our approach is based on fuzzy algebra, as well as fuzzy ontological information. We start by outlining the methodologies that may lead to the creation of a semantic index; these methodologies are integrated in a video annotating environment. Based on the semantic index, we then explain how multimedia content may be analyzed for the extraction of semantic information in the form of thematic categorization. The latter relies on stored knowledge and a fuzzy hierarchical clustering algorithm that uses a similarity measure that is based on the notion of context.
Knowledge‐Based Multimedia Content Indexing and Retrieval
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By the end of the last century the question was not whether digital archives are technically and economically viable, but rather how digital archives would be efficient and informative. In this framework, different scientific fields such as, on the one hand, development of database management systems, and, on the other hand, processing and analysis of multimedia data, as well as artificial and computational intelligence methods, have observed a close cooperation with each other during the past few years.