摘要
随着多媒体、网络、数据库等技术的不断发展,图像逐渐成为一种非常重要的媒体表现手段。如何从海量图像数据中检索出所需的资源也就成为当前研究热点之一。
基于内容的图像检索技术近年来得到长足的发展,相关反馈技术的加入使得图像检索技术向前跨越了一大步。然而,基于内容的图像检索与反馈技术仍然不能完全体现出高层语义的特征,如何缩小这个“语义鸿沟”,如何结合语义和低层特征得到更好的检索效果,这也是目前乃至未来数年内研究的难点。
本文主要致力于图像语义检索方法的研究,着重研究了相关反馈技术,提出了一种基于语义矩阵相关反馈的图像语义检索方法,同时利用感兴趣区域提出了一种结合语义和低层特征进行相关反馈的图像语义检索方法。主要研究成果如下:
1)利用语义关键字和图像构成的语义矩阵完成了对图像的一个初步的语义提取及检索。首先将图像按照颜色特征进行量化,分块后获得图像的颜色语义,映射到图像的语义关键字上,再经过基于语义矩阵的反馈技术进行了检索。实验结果表明,该方法能够进行初步的语义提取,并能够对某些类的图像获得较好的检索效果。
2)提出了一种基于感兴趣区域及相关反馈技术的图像语义检索方法。利用感兴趣区域,结合语义和低层特征,通过提取感兴趣区域的低层特征及语义信息,减少了图像内容上的冗余信息,改进了相关反馈方法,减少了反馈次数,检索效率有所改观。
3)设计实现了一个图像检索原型系统。对基于语义矩阵相关反馈的语义检索和基于感兴趣区域相关反馈的语义检索进行了验证。
With the constant development of multimedia technology, network technology and database technology, image is becoming a very important means of media representation. How to retrieve the needed resources from the mass of data is accordingly becoming one of the current research hotspots.
Content-based image retrieval technology has made rapid progress in recent years, and it took a big step forward for image retrieval technology that relevance feedback technology went into this technology. However, content-based image retrieval and relevance feedback can’t still fully reflect the features of high-level semantics, how to narrow the "semantic gap", and how to combine the semantic with the low-level features for better retrieval results, which are the current problems as well as the next few years.
The dissertation put the focus on the research of semantic-based image retrieval as well as relevance feedback technology. It proposed a semantic extraction algorithm based on a semantic matrix, using feedback technology and the semantic concept, and it also proposed another relevance feedback algorithm based on the combination of semantic and region of interest(ROI), which improved the efficiency of the semantic retrieval. The main research are as follows:
(1) The dissertation proposed an initial extraction and semantic retrieval method using the semantic keywords and semantic matrix which constituted by images. First of all, we quantified the image by the color, accordingly, we got the color of every block, which was mapped to the semantic keyword, and then we extracted semantic meaning of images through the feedback technology based on semantic matrix, finally, we retrieved the image from the image groups using this method. The result showed that this method could initially extract the semantic of images, and it could obtain better retrieval results for the certain types of images.
(2) The dissertation proposed a means of semantic-based image retrieval based on relevance feedback and ROI. We added the element of ROI to the presentation of low-level features, and took advantage of the relation of semantic features to ROI, consequently, we improved the feedback process, and appropriately reduced the number of feedback, and the retrieval efficiency could be improved.
(3) The dissertation designed an image retrieval system. The system fulfilled the two means above.
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