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Knowledge-Based Semantic Retrieval of Multimedia and Image Objects Using Collaborative Indexing

机译：基于协作索引的基于知识的多媒体和图像对象语义检索

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摘要

With the rapid development of multimedia technology, digital resources has become increasingly available and it constitutes a significant component of multimedia contents on the Internet. Since digital resources can be represented in various forms, formats, and dimensions, searching such information is far more challenging than text-based search. While some basic forms of multimedia retrieval are available on the Internet, these tend to be inflexible and have significant limitations. Currently, most of these multimedia retrieval systems mainly rely on text annotations. Here, we present an approach for deep concept-based multimedia information retrieval, which focuses on high-level human knowledge, perception, incorporating subtle nuances and emotional impression on the multimedia resources. We also provide a critical evaluation of the most common current Multimedia Information Retrieval approaches and propose an innovative adaptive method for multimedia information search that overcomes the current limitations. The main focus of our approach is concerned with image discovery and recovery by collaborative semantic indexing and user relevance feedback analysis. Through successive usage of our indexing model, novel image content indexing can be built from deep user knowledge incrementally and collectively by accumulating users’ judgment and intelligence.
机译：随着多媒体技术的飞速发展，数字资源变得越来越可用，它构成了Internet上多媒体内容的重要组成部分。由于数字资源可以各种形式，格式和尺寸表示，因此搜索此类信息比基于文本的搜索更具挑战性。尽管Internet上可以使用某些基本形式的多媒体检索，但这些形式往往不灵活并且具有很大的局限性。当前，大多数这些多媒体检索系统主要依靠文本注释。在这里，我们提出了一种基于深层概念的多媒体信息检索方法，该方法着重于人类的高级知识，感知能力，并在多媒体资源上融合了细微差别和情感印象。我们还对当前最常见的多媒体信息检索方法进行了严格的评估，并提出了一种创新的自适应方法来克服当前的局限性，用于多媒体信息搜索。我们方法的主要重点是通过协作语义索引和用户相关性反馈分析来进行图像发现和恢复。通过连续使用我们的索引模型，可以通过累积用户的判断力和智慧，逐步从深度用户知识中逐步构建新颖的图像内容索引。

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