Flexible Content Extraction and Querying for Videos

Demir, Utku
Yazıcı, Adnan
Yilmaz, Turgay
In this study, a multimedia database system which includes a semantic content extractor, a high-dimensional index structure and an intelligent fuzzy object-oriented database component is proposed. The proposed system is realized by following a component-oriented approach. It supports different flexible query capabilities for the requirements of video users, which is the main focus of this paper. The query performance of the system (including automatic semantic content extraction) is tested and analyzed in terms of speed and accuracy.


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This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused...
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An effective retrieval of multimedia data is based on its semantic content. In order to extract the semantic content, the nature of multimedia data should be analyzed carefully and the information contained should be used completely. Multimedia data usually has a complex structure containing multimodal information. Noise in the data, non-universality of any single modality, and performance upper bound of each modality make it hard to rely on a single modality. Thus, multimodal fusion is a practical approach...
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MapReduce is a popular model of executing time-consuming analytical queries as a batch of tasks on large scale data. During simultaneous execution of multiple queries, many oppor- tunities can arise for sharing scan and/or computation tasks. Executing common tasks only once can reduce the total execution time of all queries remarkably. Therefore, we propose to use Multiple Query Optimization (MQO) techniques to improve the overall performance of Hadoop Hive, an open source SQL-based distributed warehouse sy...
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Many new-generation database applications demand intelligent information management necessitating efficient interactions between database gr. knowledge bases and the users. In this study we discuss evaluation of imprecise queries in an intelligent object-oriented database environment, IFOOD. 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.
Citation Formats
U. Demir, M. KOYUNCU, A. Yazıcı, T. Yilmaz, and M. SERT, “Flexible Content Extraction and Querying for Videos,” 2011, vol. 7022, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55724.