基于压缩域图象检索技术的研究
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摘要
基于内容的图象检索技术就随着多媒体技术的发展应运而生。它的主要研究内容是根据自动获得的图象特征，从图象数据库中检索出相关图象。近年来，随着许多压缩标准(如JPEG、MPEG、H.261等)的制定和推广，压缩格式的图象使用越来越普遍和广泛。直接对压缩格式的图象进行检索的技术就成为了基于内容的图象检索技术的一个重要的趋势。
本论文围绕压缩域图象检索中的一些关键方法，包括基于离散余弦变换和基于矢量量化等方法联合提取图象低层特征、图象间相似性度的度量等技术，进行了一些探索性的研究。主要研究了：
1、基于颜色和纹理的一些特征提取算法以及图象间的相似度度量方法。并深入研究了当前压缩域图象检索技术的发展现状；
2、基于DCT域的图象检索方法，并提出了一种基于重组DCT系数检索图象的方法。
3、基于矢量量化的图象检索方法，研究对比了标量量化、矢量量化以及分类矢量量化等不同量化方法及其统计特征量用于图象检索的性能。
研究的内容属于目前图象检索领域的研究热点，具有一定的理论意义和实用价值。
本论文的贡献：
提出了一种将JPEG图象的DCT系数按照多分辨率小波变换的形式进行重组，得到若干子带并建立子带能量直方图作为特征，在按照Morton的顺序建立索引，并对索引采用变形B树数据结构组织，进行检索的方法。本方法的检索时间和数据库大小无关，而仅仅与图象子带数目相关，大量的实验证明本方法极大的降低了检索时间。
The technique of content-based image retrieval (CBIR) was come into being with the steady growth of multimedia technique, the main content of this technique is to retrieval relevant images from image database based on automatically derived image features. In recent years, with the development and spread of many compression standard (JPEG, MPEG, H.261, etc), compressed image was used more and more popular and widely. So retrieval operation directly in compressed formatted image becomes a new important trend of CBIR.
In this dissertation, lots of exploratory research work has been done around some key techniques of Image Retrieval Based On Compressed-Domain, which include based on Discrete Cosine Transform (DCT) , based on Vector Quantization (VQ) combine with low-level feature extraction, similarity measure and so on. The emphasis of this dissertation:
Firstly, some feature extraction algorithms based on color and texture are analyzed and discussed, and made a full-scale discussion of the current compressed domain retrieval techniques.
Next, In technique of image retrieval based on DCT compressed-domain. An image retrieval approach based on DCT coefficients reorder is proposed.
Finally, In technique of image retrieval based on Vector Quantization, analyzed and compared scalar quantization, vector quantization and classified vector quantization using statistical features for the performances of image retrieval.
The presented study is the current research hotspot of image retrieval. Thus its research has both theory and application value.
The contribution of this dissertation:
An image retrieval approach based on DCT compressed domain is proposed. First, reorder DCT coefficients using multiresolution wavelet transform, then build subband energy histograms formed from reordered DCT coefficients of database images, build indices of images by using Morton order and order database for indexing by using variant B-tree data structure. Many experimental results show that this approach is fast and effective.
本论文围绕压缩域图象检索中的一些关键方法，包括基于离散余弦变换和基于矢量量化等方法联合提取图象低层特征、图象间相似性度的度量等技术，进行了一些探索性的研究。主要研究了：
1、基于颜色和纹理的一些特征提取算法以及图象间的相似度度量方法。并深入研究了当前压缩域图象检索技术的发展现状；
2、基于DCT域的图象检索方法，并提出了一种基于重组DCT系数检索图象的方法。
3、基于矢量量化的图象检索方法，研究对比了标量量化、矢量量化以及分类矢量量化等不同量化方法及其统计特征量用于图象检索的性能。
研究的内容属于目前图象检索领域的研究热点，具有一定的理论意义和实用价值。
本论文的贡献：
提出了一种将JPEG图象的DCT系数按照多分辨率小波变换的形式进行重组，得到若干子带并建立子带能量直方图作为特征，在按照Morton的顺序建立索引，并对索引采用变形B树数据结构组织，进行检索的方法。本方法的检索时间和数据库大小无关，而仅仅与图象子带数目相关，大量的实验证明本方法极大的降低了检索时间。
The technique of content-based image retrieval (CBIR) was come into being with the steady growth of multimedia technique, the main content of this technique is to retrieval relevant images from image database based on automatically derived image features. In recent years, with the development and spread of many compression standard (JPEG, MPEG, H.261, etc), compressed image was used more and more popular and widely. So retrieval operation directly in compressed formatted image becomes a new important trend of CBIR.
In this dissertation, lots of exploratory research work has been done around some key techniques of Image Retrieval Based On Compressed-Domain, which include based on Discrete Cosine Transform (DCT) , based on Vector Quantization (VQ) combine with low-level feature extraction, similarity measure and so on. The emphasis of this dissertation:
Firstly, some feature extraction algorithms based on color and texture are analyzed and discussed, and made a full-scale discussion of the current compressed domain retrieval techniques.
Next, In technique of image retrieval based on DCT compressed-domain. An image retrieval approach based on DCT coefficients reorder is proposed.
Finally, In technique of image retrieval based on Vector Quantization, analyzed and compared scalar quantization, vector quantization and classified vector quantization using statistical features for the performances of image retrieval.
The presented study is the current research hotspot of image retrieval. Thus its research has both theory and application value.
The contribution of this dissertation:
An image retrieval approach based on DCT compressed domain is proposed. First, reorder DCT coefficients using multiresolution wavelet transform, then build subband energy histograms formed from reordered DCT coefficients of database images, build indices of images by using Morton order and order database for indexing by using variant B-tree data structure. Many experimental results show that this approach is fast and effective.
引文
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