Knowledge-assisted content based retrieval for multimedia databases

Unlike conventional databases, which manage only text and numerical data, multimedia databases must evaluate audio and visual properties of data. We propose a system of querying and content-based retrieval that considers audio or visual properties of multimedia data     

一种基于OFDM系统的无线图像的有效传输方法

本文提出一种基于OFDM多载波系统的无线图像传输的方法。该方法将基于FFT／IFFT变换的OFDM多载波调制与基于小波变换的SPIHT嵌入式零树编码相结合，首先将SPIHT输出码流分成若干数据块，然后根据各数据块视觉重要性的不同，给其分配相应的子信道和发送功率，使视觉重要性高的码流在较低的误比特率下进行传输，实现对视觉重要性不同的码流的不等错误保护。本文在瑞利平坦衰落信道下的仿真结果表明：在低信噪比下，采用本文提出的不等错误保护方法比等错误保护方法的峰值信噪比增益要高出15dB以上。     

FIRE: fractal indexing with robust extensions for image databases

As already documented in the literature, fractal image encoding is a family of techniques that achieves a good compromise between compression and perceived quality by exploiting the self-similarities present in an image. Furthermore, because of its compactness and stability, the fractal approach can be used to produce a unique signature, thus obtaining a practical image indexing system. Since fractal-based indexing systems are able to deal with the images in compressed form, they are suitable for use with large databases. We propose a system called FIRE, which is then proven to be invariant under three classes of pixel intensity transformations and under geometrical isometries such as rotations by multiples of /spl pi//2 and reflections. This property makes the system robust with respect to a large class of image transformations that can happen in practical applications: the images can be retrieved even in the presence of illumination and/or color alterations. Additionally, the experimental results show the effectiveness of FIRE in terms of both compression and retrieval accuracy.     

Frequency layered color indexing for content-based image retrieval

Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method.     

Histological image retrieval based on semantic content analysis

The demand for automatic recognition and retrieval of medical images for screening, reference, and management is increasing. We present an intelligent content-based image retrieval system called I-Browse, which integrates both iconic and semantic content for histological image analysis. The I-Browse system combines low-level image processing technology with high-level semantic analysis of medical image content through different processing modules in the proposed system architecture. Similarity measures are proposed and their performance is evaluated. Furthermore, as a byproduct of semantic analysis, I-Browse allows textual annotations to be generated for unknown images. As an image browser, apart from retrieving images by image example, it also supports query by natural language.     

Data hiding approach for efficient image indexing

A data hiding approach to embed the indexing keys inside the JPEG compressed images for their retrieval, inspired from the spirit of digital watermarking, is proposed. At the stage of database population, the existing approach is to compress all images by standard JPEG in order to save their storage space, but this ignores the compression of their indexing keys under the notion that the size of each indexing key is negligible. When a large image database is established, however, this part of storage space becomes non-trivial. By hiding the indexing keys inside the JPEG compressed codes, significant advantages are gained in that the indexing keys can be compressed naturally by JPEG without any additional cost. Experiments on hiding an indexing key of 256 bytes illustrate that: no noticeable distortion is introduced in comparison with non-watermarked images; and their storage space is reduced into the region of 7-27% of their original size.     

An efficient and effective region-based image retrieval framework

An image retrieval framework that integrates efficient region-based representation in terms of storage and complexity and effective on-line learning capability is proposed. The framework consists of methods for region-based image representation and comparison, indexing using modified inverted files, relevance feedback, and learning region weighting. By exploiting a vector quantization method, both compact and sparse (vector) region-based image representations are achieved. Using the compact representation, an indexing scheme similar to the inverted file technology and an image similarity measure based on Earth Mover's Distance are presented. Moreover, the vector representation facilitates a weighted query point movement algorithm and the compact representation enables a classification-based algorithm for relevance feedback. Based on users' feedback information, a region weighting strategy is also introduced to optimally weight the regions and enable the system to self-improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.     

XML规范化索引树构造算法

参照关系数据库中的约束理论,给出应用于XML文档的函数依赖规则和三范式理论,去除XML树中的部分函数依赖和传递函数依赖,避免了更新异常,并根据规范化结果重新组合成XML规范化语义树,通过在XML原树中标记出语义树而产生一棵XML规范化索引树NIT,基于这棵树完成XML文档的访问操作.     

Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing

Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.     

基于Android和内容的图像检索系统设计

在移动计算平台下对基于内容的图像检索技术进行了探讨,采用上位机处理运算、下位机展示检索结果的策略,在Android平台下,实现了利用HSV颜色特征和灰度共生矩阵纹理特征的图像检索。系统运行稳定,检索速度快,准确率较高,对移动终端下图像检索技术的应用与推广起到了推动作用。     

一种有效的基于内容的图像检索方法

本文针对基于内容的图像检索中特征和相似度问题，提出新的距离模式，并以彩色空间中扩展共发矩阵作为纹理描述，在测试系统iPhoto上，数据库为56600幅图像时，实验结果显示，本文方法优于传统方法。     

Content based image indexing and retrieval using directional local extrema and magnitude patterns

In this paper, we integrate the concept of directional local extremas and their magnitude based patterns for content based image indexing and retrieval. The standard ditectional local extrama pattern (DLEP) extracts the directional edge information based on local extrema in 0°, 45°, 90°, and 135° directions in an image. However, they are not considering the magnitudes of local extremas. The proposed method integrates these two concepts for better retrieval performance. The sign DLEP (SDLEP) operator is a generalized DLEP operator and magnitude DLEP (MDLEP) operator is calculated using magnitudes of local extremas. The performance of the proposed method is compared with DLEP, local binary patterns (LBPs), block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG) and local edge patterns for image retrieval (LEPINV) methods by conducting two experiments on benchmark databases, viz. Corel-5K and Corel-10K databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to other existing methods on respective databases.     

Structured representations in a content based image retrieval context

Here, we propose an automatic system to annotate and retrieve images. We assume that regions in an image can be described using a vocabulary of blobs. Blobs are generated from image features using clustering. Features are locally extracted on regions to capture Color, Texture and Shape information. Regions are processed by an efficient segmentation algorithm. Images are structured into a region adjacency graph to consider spatial relationships between regions. This representation is used to perform a similarity search into an image set. Hence, the user can express his need by giving a query image, and thereafter receiving as a result all similar images. Our graph based approach is benchmarked to conventional Bag of Words methods. Results tend to reveal a good behavior in classification of our graph based solution on two publicly available databases. Experiments illustrate that a structural approach requires a smaller vocabulary size to reach its best performance.     

An efficient multi-exponentiation scheme based on modified Booth's method

We propose an efficient multi-exponentiation algorithm based on the modified Booth' algorithm and Montgomery's modular multiplication algorithm. The multi-exponentiation algorithm can be used to implement fast modern cryptosystems. Owing to the reduced number of multiplications, this algorithm is about 10% faster than Pekmestzi's algorithms. The proposed algorithm can be implemented in hardware as a small component. The component can then be used to form an efficient modular multi-exponentiation module by combining it with an efficient Montgomery modular multiplication module.     

一种基于量子机制的图像显著性检测方案

提出了一种与传统方法相比效率更高的量子图像显著性检测方案.为了在量子计算机中表示和存储RGB图像,并计算不同像素间的反差,此方案采用3量子位描述颜色信息,把2轨×2图像矩阵编码为量子叠加态;结合Hadamard门和受控旋转算子,计算基态概率幅可反映像素在RGB三通道上的全局颜色反差;通过有限次数的投影测量可得到像素的归一化颜色反差及位置信息,并构建显著图.给出了相关量子电路的实现和复杂度分析.与多种传统显著性检测算法进行了对比实验,结果表明提出的方案具有良好的检测效果和更高的检测效率.     

An integrated content and metadata based retrieval system for art

A new approach to image retrieval is presented in the domain of museum and gallery image collections. Specialist algorithms, developed to address specific retrieval tasks, are combined with more conventional content and metadata retrieval approaches, and implemented within a distributed architecture to provide cross-collection searching and navigation in a seamless way. External systems can access the different collections using interoperability protocols and open standards, which were extended to accommodate content based as well as text based retrieval paradigms. After a brief overview of the complete system, we describe the novel design and evaluation of some of the specialist image analysis algorithms including a method for image retrieval based on sub-image queries, retrievals based on very low quality images and retrieval using canvas crack patterns. We show how effective retrieval results can be achieved by real end-users consisting of major museums and galleries, accessing the distributed but integrated digital collections.