机动目标的逆合成孔径雷达成像原理与算法

对于非合作的机动目标,由于目标相对于雷达射线的姿态和转速难以测定,而且是时变的,因而给逆合成孔径雷达(ISAR)成像造成较大困难.本文讨论了这种情况下成像的一般原理,并对机动性不太大,散射点子回波多普勒变化满足一阶近似条件时,提出了实用算法.实测数据的处理结果说明新算法是可行的.     

Generic content-based audio indexing and retrieval framework

Rapid increase in the amount of the digital audio collections presenting various formats, types, durations and other parameters that the digital multimedia world refers demands a generic framework for robust and efficient indexing and retrieval based on the aural content. Moreover, from the content-based multimedia retrieval point of view, the audio information can be even more important than the visual part as it is mostly unique and significantly stable within the entire duration of the content. A generic and robust audio-based multimedia indexing and retrieval framework, which has been developed and tested under the MUVIS system, is presented. This framework supports the dynamic integration of the audio feature extraction modules during the indexing and retrieval phases and therefore provides a test-bed platform for developing robust and efficient aural feature extraction techniques. Furthermore, the proposed framework is designed based on the high-level content classification and segmentation in order to improve the speed and accuracy of the aural retrievals. Both theoretical and experimental results are finally presented, including the comparative measures of retrieval performance with respect to the visual counterpart.     

An Architecture for Service Quality Management Using Cooperative Communications

The rapid enhancement and explosive expansion of mobile communication systems will lead to an increased demand for multimedia services. Various multimedia contents will be available due to the proliferation of IPTV, video on demand, user-generated content, and content-centric networking. Therefore, multimedia service quality is becoming a critical issue. The static allocation of network resources may not be sufficient to guarantee the required service quality of multimedia in wireless networks. For ensuring the service quality, it is important to manage the resources considering the overall performance of the network and the end system. In particular, a certain level of service quality should be maintained regardless of the network situation. To achieve this objective, we first propose an architecture for service quality management and then present a detailed framework and mechanisms to maintain the desired service quality using the cooperation between communication entities in wireless networks. Simulation results are also presented to show that the proposed framework works well in wireless networks.     

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

In this paper, a complete solution for search and retrieval of rich multimedia content over modern databases is presented. The framework proposed in this paper combines the advantages of multimodal search with those of annotation propagation into a unified system. Moreover, an effective technique, which is appropriate for large-scale indexing, is adopted, extended and integrated to the proposed framework so as to achieve optimized search and retrieval of rich media content even from large-scale databases.     

Multi-manifold-based skin classifier on feature space Voronoï regions for skin segmentation

Skin segmentation is a crucial and a challenging step in many face and gesture recognition techniques and it has various applications in human computer interaction, objectionable content filtering, image retrieval and many more. In this article, we propose a novel skin segmentation method, which uses multi-manifold-based skin classification of feature space skin candidate Voronoï regions to achieve accurate skin segmentation. The state-of-the-art skin segmentation techniques reported in this article focus on discrimination between textural feature vectors belonging to skin and non-skin classes. In contrast, the proposed method focuses on discrimination between textural feature vectors belonging to skin and skin-like (non-skin) classes, which lead to higher skin classification accuracy. Furthermore, we introduce a novel image segmentation technique based on spatial and feature space Dirichlet tessellation (also called a Voronoï diagram) to achieve feature space segmentation of skin candidate regions of an image. These feature space segments will then be classified using a multi-manifold-based skin classifier. The proposed skin segmentation method was evaluated on two benchmark skin segmentation data sets and its results were compared with four other state-of-the-art methods proposed for skin segmentation. The experimental results reported in this article confirm that the proposed method outperforms the existing skin segmentation approaches in terms of false alarm rates in the skin segmentation process. Also, the proposed method results in the lowest minimal detection error compared to the existing methods reported in this article.     

Semantic content-based image retrieval: A comprehensive study

The complexity of multimedia contents is significantly increasing in the current digital world. This yields an exigent demand for developing highly effective retrieval systems to satisfy human needs. Recently, extensive research efforts have been presented and conducted in the field of content-based image retrieval (CBIR). The majority of these efforts have been concentrated on reducing the semantic gap that exists between low-level image features represented by digital machines and the profusion of high-level human perception used to perceive images. Based on the growing research in the recent years, this paper provides a comprehensive review on the state-of-the-art in the field of CBIR. Additionally, this study presents a detailed overview of the CBIR framework and improvements achieved; including image preprocessing, feature extraction and indexing, system learning, benchmarking datasets, similarity matching, relevance feedback, performance evaluation, and visualization. Finally, promising research trends, challenges, and our insights are provided to inspire further research efforts.     

Perceptual quality‐based image communication service framework for wireless sensor networks

Wireless multimedia sensor networks (WMSNs) have an increasing variety of multimedia‐based applications including image and video transmission. In these types of applications, multimedia sensor nodes should ideally maximize perceptual quality and minimize energy expenditures in communication. For the required perceptual quality to be obtained, quality‐aware routing is a key research area in WMSNs. However, mapping the system parameters to the end user's perceptual quality‐of‐service measures is a challenging task because of incomplete identification metrics. Unfortunately, unless disputable assumptions and simplifications are made, optimal routing algorithm is not tractable. In this paper, we propose a novel image transmission framework to optimize both perceptual quality and energy expenditure in WMSNs. Our framework aims to provide acceptable perceptual quality at the end user by using an analytical distortion prediction model that is able to predict the image distortion resulting from any given error pattern. The innovation of the proposed scheme lies in the combined use of a content‐aware packet prioritization with an energy‐aware and quality‐aware routing protocol, named as image quality‐aware routing. Additionally, it does not only propose an energy‐efficient route selection policy but also manages the network load according to the energy residues of nodes, thus leading to a great energy economy. The results reveal that the framework is capable of identifying true metrics for mapping required image quality to network parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

Medical image categorization and retrieval for PACS using the GMM-KL framework.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.     

Spoken document understanding and organization

Spoken documents (or associated multimedia content) are in fact better understood and reorganized in a way that retrieval/browsing can be performed easily. For example, they are now in the form of short paragraphs, properly organized in some hierarchical visual presentation with titles/summaries/topic labels as references for retrieval and browsing. The retrieval can be performed based on the full content, the summaries/titles/topic labels, or both. In this article, this is referred to as spoken document understanding and organization for efficient retrieval/browsing applications. The purpose of this article is to present a concise, comprehensive, and integrated overview of related areas in a unified context of spoken document understanding and organization for efficient retrieval/browsing applications. In addition, we present an initial prototype system we developed at National Taiwan University as a new example of integrating the various technologies and functionalities.     

基于HSV模型和特征点匹配的行人重识别算法

提出了一种基于HSV模型和特征点匹 配相结合的行 人重识别算法。首先根据改进的HSV空间颜色量化策略,比对两幅行人图像的 躯干和腿部主颜色是否一致,以快速确定备选目标;然后对备选目标,利用环形Gabor滤波器组生成多尺度图 像,再利用改进的FAST算法和BRIEF算法对多尺度图像进行特征点提取与描述,最后利用暴 力算法和随机抽样一 致性算法进行特征点匹配和提纯,以达到较好的匹配效果。实验结果表明,本文提出的行人 重识别算法具有较高的识别准确率,识别速度达到12frames。     

A shape-based image retrieval method using salient edges

Content-based image retrieval is emerging as an important research area with applications in digital libraries and multimedia databases. In this paper, we present a novel five-stage image retrieval method based on salient edges. In the first stage, the Canny operator is performed to detect edge points. Then, the Water-Filling algorithm is employed to extract edge curves. In the third stage, salient edges are selected and the shape features in terms of the salient edges are yielded. In the fourth stage, a similarity measure, namely the integrated salient edge matching, that integrates properties of all the salient edges, is introduced, and used to compare the similarity of the query image with the images in the database. Finally, the best matches are returned in similarity order. The presented approach is easy to implement and can be efficiently applied to retrieve images with clear edges. Preliminary experimental results on a database containing 6500 images are very promising.     

Camera constraint-free view-based 3-D object retrieval

Recently, extensive research efforts have been dedicated to view-based methods for 3-D object retrieval due to the highly discriminative property of multiviews for 3-D object representation. However, most of state-of-the-art approaches highly depend on their own camera array settings for capturing views of 3-D objects. In order to move toward a general framework for 3-D object retrieval without the limitation of camera array restriction, a camera constraint-free view-based (CCFV) 3-D object retrieval algorithm is proposed in this paper. In this framework, each object is represented by a free set of views, which means that these views can be captured from any direction without camera constraint. For each query object, we first cluster all query views to generate the view clusters, which are then used to build the query models. For a more accurate 3-D object comparison, a positive matching model and a negative matching model are individually trained using positive and negative matched samples, respectively. The CCFV model is generated on the basis of the query Gaussian models by combining the positive matching model and the negative matching model. The CCFV removes the constraint of static camera array settings for view capturing and can be applied to any view-based 3-D object database. We conduct experiments on the National Taiwan University 3-D model database and the ETH 3-D object database. Experimental results show that the proposed scheme can achieve better performance than state-of-the-art methods.     

Multimedia Retrieval and Delivery: Essential Metadata Challenges and Standards

Multimedia information retrieval (MIR) and delivery plays an important role in many application domains due to the increasing need to identify, filter, and manage growing amounts of data, notably multimedia information. To efficiently manage and exchange multimedia information, interoperability between coded data and metadata is required and standardization is central to achieving the necessary level of interoperability. In the context of this paper, the term retrieval refers to the process by which a user, human or machine, identifies the content it needs, and the term delivery refers to the adaptive transport and consumption of the identified content in a particular context or usage environment. Both the retrieval and delivery processes may require content and context metadata. This paper will argue that maximum quality of experience depends not only on the content itself (and thus content metadata) but also on the consumption conditions (thus context metadata). Additionally, the rights and protection conditions have become critically important in recent years, especially with the explosion of electronic music commerce and different ldquoshoppingrdquo conditions. This paper will review existing multimedia standards related to information retrieval and adaptive delivery of multimedia content, emphasizing the need for such standards, and will show how these standards can help the development, dissemination, and valorization of MIR research results. Moreover, it will also discuss limitations of the current standards and anticipate what future standardization activities are relevant and needed. Due to space limitations, the paper will mainly concentrate on MPEG standards although many other relevant standards are also reviewed and discussed.     

微博数据获取技术及展望

作为一种新兴的社会媒体形式,微博集博客、媒体、即时通讯功能于一身,兼具平台多样、传播迅速、交互性强等特点,在舆论传播方面发挥了重要作用。文中介绍了微博的基本概念,系统概括了微博数据获取的主流方法,总结了微博数据获取条件的选择方法和微博数据的表达方法,并讨论了数据获取中的瓶颈问题,给出了该领域中尚需解决的关键问题以及未来的发展方向。     

Design and implementation of geo-tagged video search framework

User generated video content is experiencing significant growth which is expected to continue and further accelerate. As an example, users are currently uploading 20 h of video per minute to YouTube. Making such video archives effectively searchable is one of the most critical challenges of multimedia management. Current search techniques that utilize signal-level content extraction from video struggle to scale.Here we present a framework based on the complementary idea of acquiring sensor streams automatically in conjunction with video content. Of special interest are geographic properties of mobile videos. The meta-data from sensors can be used to model the coverage area of scenes as spatial objects such that videos can effectively, and on a large scale, be organized, indexed and searched based on their field-of-views. We present an overall framework that is augmented with our design and implementation ideas to illustrate the feasibility of this concept of managing geo-tagged video.     

Indexing and retrieval of images by spatial constraints

Many multimedia applications require retrieval of spatially similar images against a given query image. Existing work on image retrieval and indexing either requires extensive low-level computations or elaborate human interaction. In this paper, we introduce a new symbolic image representation technique to eliminate repetitive tasks of image understanding and object processing. Our symbolic image representation scheme is based on the concept of hierarchical decomposition of image space into spatial arrangements of features while preserving the spatial relationships among the image objects. Quadtrees are used to manage the decomposition hierarchy and play an important role in defining the similarity measure. This scheme is incremental in nature, can be adopted to accommodate varying levels of details in a wide range of application domains, and provides geometric variance independence. While ensuring that there are no false negatives, our approach also discriminates against non-matching entities by eliminating them as soon as possible, during the coarser matching phases. A hierarchical indexing scheme based on the concept of image signatures and efficient quadtree matching has been devised. Each level of the hierarchy tends to reduce the search space, allowing more involved comparisons only for potentially matching candidate database images. For a given query image, a facility is provided to rank-order the retrieved spatially similar images from the image database for subsequent browsing and selection by the user.     

基于边缘方向直方图的形状检索

近年来,随着多媒体技术和数字设备的出现,如何有效地管理和访问图像信息已成为人们亟待解决的问题.因此,一种新的图像检索技术——基于内容的图像检索技术被提出来.其中,由于图像的形状特征更符合人们的视觉感知,因此基于形状的图像检索越来越受到研究者的关注.旨在研究基于形状轮廓特征的图像检索,提出了基于边缘方向的直方图形状检索算法.通过对常用边缘检测算子的分析和比较,给出了边缘方向直方图特征提取的具体实现技术,对采用的特征匹配方法做了描述,最后通过实验的结果与分析验证了算法的性能.