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首页> 外文会议>2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare >“An ABIR and CBIR fusion based techniques for associated image retrieval”
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“An ABIR and CBIR fusion based techniques for associated image retrieval”

机译：“基于ABIR和CBIR融合的相关图像检索技术”

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This paper provides the information about image retrieval using the concept of Attribute Base Image Retrieval (ABIR) and Content Base Image Retrieval (CBIR). The retrieval method nominate in paper make use of the fusion of the images multimodal information (visual and textual) which is a recent course in image retrieval researches. In this present paper we will first focus on content based image retrieval (CBIR) for retrieving the required image from large databases. The main goal of content based image retrieval is to efficiently retrieve images that are visually similar to a query image. Image retrieval is the science of locating images from a large database or image sequences that fulfill the specified image need. An image retrieval technique is based on the Scale-Invariant Feature Transform (SIFT) method which is present in this project. It joins two different data mining techniques to get semantically related images these are: association and clustering rules mining algorithm which is nothing but the Bag of Features (BoF). BoF methods are based on order less collection of quantized local image descriptors they remove spatial information and are therefore computationally and conceptually simpler than many alternative methods.
机译：本文提供了使用属性基础图像检索（ABIR）和内容基础图像检索（CBIR）的概念进行图像检索的信息。论文中提出的检索方法是利用图像多模态信息（视觉和文本）的融合，这是图像检索研究的新方向。在本文中，我们将首先关注基于内容的图像检索（CBIR），以从大型数据库中检索所需的图像。基于内容的图像检索的主要目标是有效地检索在视觉上类似于查询图像的图像。图像检索是从大型数据库或满足指定图像需求的图像序列中定位图像的科学。图像检索技术基于该项目中存在的尺度不变特征变换（SIFT）方法。它结合了两种不同的数据挖掘技术来获取与语义相关的图像，它们是：关联和聚类规则挖掘算法，不过这只是功能包（BoF）。 BoF方法基于量化局部图像描述符的无序集合，它们删除了空间信息，因此比许多替代方法在计算和概念上更简单。

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