In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature. The obtained results are fused to reach a decision about the component. Then, a fuzzy object extractor determines the whole object considering the detected components and their geometric configurations. The fuzzy object extractor is a fuzzy inference engine which tests various combinations of detected components and their fuzzified directions and distances. The initial tests yield promising results and encourage further studies to extend proposed method.

Support Vector Machines (SVMs) have been extensively used for visual object classification to bridge the semantic gap between the low level features and high level concepts. SVM treats each training input equally during the construction of its decision surface which results in poor learning machines if training data include outliers. In this paper, a novel fuzzy visual object classification approach utilizing Self-Organizing Maps (SOMs) in SVM is proposed. The experimental results show the effectiveness of the proposed Fuzzy SVM compared to the traditional SVM.

Multimedia databases have emerged to cope up with the huge amount of multimedia data, which comes up as a result of technological advancement. However, more intelligent techniques are required to satisfy different query requirements of multimedia users. This study extends the query capability of a multimedia database through the integration of a fuzzy rule-based system. In addition to fuzzy semantic rules, which deduce new information from the data stored in the database, fuzzy spatial and temporal relations, which are inherent to multimedia applications, are defined in the rule-based system. Users can formulate fuzzy semantic, spatial, temporal, and spatiotemporal queries, resulting in the deduction of new information using the rules defined in the rule-based system. With some practical examples, the paper presents how a fuzzy rule-based system integrated to a fuzzy multimedia database improves the query capabilities of the database system intelligently. © 2011 Wiley Periodicals, Inc.

This study presents a new comprehensive framework for semantic content extraction from raw video, storage of the extracted data and retrieval of the stored data. Objects, spatial relations between objects, events and temporal relations between events, which are considered as semantic contents of the video, are extracted automatically to a certain extend with the developed approach. Extraction process is supported by manual annotation when automatic extraction is not satisfactory. The extracted information is stored in an intelligent fuzzy object-oriented database in which the database is enhanced with a fuzzy knowledge-based system. Domain specific deduction rules can be defined to derive new information about semantic contents of the video. The database is also supported by an access structure to increase retrieval efficiency. The proposed framework is capable of handling uncertain data arising from annotation process or video nature.

The size of multimedia data is increasing fast due to the abundance of multimedia applications. Modeling the semantics of the data effectively is crucial for proper management of it. In this paper, we present a fuzzy conceptual data model for multimedia data which is also generic in the sense that it can be adapted to all multimedia domains. The model takes an object-oriented approach and it handles fuzziness at different representation levels where fuzziness is inherent in multimedia applications and should be properly modeled. The proposed model also has the nice feature of representing the structural hierarchy of multimedia data as well as the spatial and temporal relations of the data. The model is applied to the news video domain and implemented as a fuzzy multimedia database system where it turns out to be effective in representing the domain and thereby provides an evidence for the general applicability of the model. The model is accompanied by an automatic multimedia annotation scheme which makes use of information extraction techniques on the corresponding multimedia texts.

A fuzzy object-oriented data model is a fuzzy logic-based extension to an object-oriented database model that permits uncertain data to be explicitly represented. The fuzzy object-oriented database (FOOD) model is one of the proposed models in the literature to handle uncertainty in object-oriented databases. Several kinds of fuzziness are dealt with in the FOOD model, including fuzziness at attribute level and between object and class and between class and superclass relations. The traditional index structures do not allow efficient access to both crisp and fuzzy objects for fuzzy object-oriented databases since they are not efficient enough in processing both crisp and fuzzy queries. In this study, we propose a new index structure, namely a FOOD index (FI), to deal with different kinds of fuzziness in fuzzy object-oriented databases and to support multidimensional indexing. In this paper, we describe this proposed index structure and show how it supports various types of flexible queries, and evaluate its performance for exact, range, and fuzzy queries.

For many knowledge-intensive applications, it is important to develop an environment that permits flexible modeling and fuzzy querying of complex data and knowledge including uncertainty. With such an environment, one can have intelligent retrieval of information and knowledge, which has become a critical requirement for those applications. In this paper, we introduce a fuzzy knowledge-based (FKB) system along with the model and the inference mechanism. The inference mechanism is based on the extension of the Rete algorithm to handle fuzziness using a similarity-based approach. The proposed FKB system is used in the intelligent fuzzy object-oriented database (IFOOD) environment, in which a fuzzy object-oriented database is used to handle large scale of complex data while the FKB system is used to handle knowledge of the application domain. Both the fuzzy object-oriented database system and the fuzzy knowledge-based system are based on the object-oriented concepts to eliminate data type mismatches. The aim of this paper is mainly to introduce the FKB system of the IFOOD environment.

Next generation information system applications require powerful and intelligent information management that necessitates an efficient interaction between database and knowledge base technologies. It is also important for these applications to incorporate uncertainty in data objects, in integrity constraints, and/or in application. In this study, we propose an intelligent object-oriented database architecture, FOOD, which permits the flexible modeling and querying of complex data and knowledge including uncertainty with powerful retrieval capability.

Many new-generation database applications demand intelligent information management necessitating efficient interactions between database & knowledge bases and the users. In this study we discuss evaluation of imprecise queries in an intelligent object-oriented database environment, WOOD. A flexible query evaluation mechanism, capable of handling different data types including complex and imprecise data and knowledge is presented and key language issues are addressed. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.