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.     

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.     

综合语义与颜色特征的图像检索技术研究

针对多媒体搜索引擎系统中的图像检索技术，本文提出了应用图像的高层语义特征和底层颜色特征作为图像检索的综合指标，将图像文本和视觉信息融合起来，给出了一种综合语义和颜色特征的图像检索系统的体系架构．以填补多媒体底层特征和高层语义之间的差异，并在此基础上提出了相关算法，使图像检索能够满足用户的需求．提高图像检索的效率和精度。     

Content-based image retrieval using H.264 intra coding features

Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images.     

一种基于区域的动态分块图象检索方法

本文提出一种新颖的基于内容和图像检索方法，基于运动子块分割并根据视觉特性对不同区域做不同的加权，比较各子块相似度，分析相似度矩阵，并检索查询物体。通过将图象分割细化，充分利用了原图的颜色位置信息，通过实验，实现了对特定物体进行检索。该物体检索方法可进一步发展，为特定的后续处理奠定基础，如在人脸识别等功能中发挥重要作用。     

An Attention-Based Approach to Content-Based Image Retrieval

Mark Weiser's vision that ubiquitous computing will overcome the problem of information overload by embedding computation in the environment is on the verge of becoming a reality. Nevertheless today's technology is now capable of handling many different forms of multimedia that pervade our lives and as a result is creating a healthy demand for new content management and retrieval services. This demand is everywhere; it is coming from the mobile videophone owners, the digital camera owners, the entertainment industry, medicine, surveillance, the military, and virtually every library and museum in the world where multimedia assets are lying unknown, unseen and unused.The volume of visual data in the world is increasing exponentially through the use of digital camcorders and cameras in the mass market. These are the modern day consumer equivalents of ubiquitous computers, and, although storage space is in plentiful supply, access and retrieval remain a severe bottle-neck both for the home user and for industry. This paper describes an approach, which makes use of a visual attention model together with a similarity measure, to automatically identify salient visual material and generate searchable metadata that associates related items in a database. Such a system for content classification and access will be of great use in current and future pervasive environments where static and mobile content retrieval of visual imagery is required.     

Image indexing and retrieval using expressive fuzzy description logics

The effective management and exploitation of multimedia documents requires the extraction of the underlying semantics. Multimedia analysis algorithms can produce fairly rich, though imprecise information about a multimedia document which most of the times remains unexploited. In this paper we propose a methodology for semantic indexing and retrieval of images, based on techniques of image segmentation and classification combined with fuzzy reasoning. In the proposed knowledge-assisted analysis architecture a segmentation algorithm firstly generates a set of over-segmented regions. After that, a region classification process is employed to assign semantic labels using a confidence degree and simultaneously merge regions based on their semantic similarity. This information comprises the assertional component of a fuzzy knowledge base which is used for the refinement of mistakenly classified regions and also for the extraction of rich implicit knowledge used for global image classification. This knowledge about images is stored in a semantic repository permitting image retrieval and ranking. This research was supported by the European Commission under contract FP6-027026 K-SPACE.     

Models for motion-based video indexing and retrieval

With the rapid proliferation of multimedia applications that require video data management, it is becoming more desirable to provide proper video data indexing techniques capable of representing the rich semantics in video data. In real-time applications, the need for efficient query processing is another reason for the use of such techniques. We present models that use the object motion information in order to characterize the events to allow subsequent retrieval. Algorithms for different spatiotemporal search cases in terms of spatial and temporal translation and scale invariance have been developed using various signal and image processing techniques. We have developed a prototype video search engine, PICTURESQUE (pictorial information and content transformation unified retrieval engine for spatiotemporal queries) to verify the proposed methods. Development of such technology will enable true multimedia search engines that will enable indexing and searching of the digital video data based on its true content.     

Content-based retrieval of dynamic PET functional images

The recent information explosion has led to a massively increased demand for multimedia data storage in integrated database systems. Content-based retrieval is an important alternative and complement to traditional keyword-based searching for multimedia data and can greatly enhance information management. However, current content-based image retrieval techniques have some deficiencies when applied in the biomedical functional imaging domain. In this paper, we presented a prototype design for a content-based functional image retrieval database system for dynamic positron emission tomography (PET). The system supports efficient content-based retrieval based on physiological kinetic features and reduces image storage requirements. This design makes it possible to maintain a large number of patient data sets online and to rapidly retrieve dynamic functional image sequences for the interpretation and generation of physiological parametric images, and offers potential advantages in medical image data management and telemedicine, as well as providing possible opportunities in the statistical and comparative analysis of functional image data     

A new way for multidimensional medical data management: volume of interest (VOI)-based retrieval of medical images with visual and functional features.

The advances in digital medical imaging and storage in integrated databases are resulting in growing demands for efficient image retrieval and management. Content-based image retrieval (CBIR) refers to the retrieval of images from a database, using the visual features derived from the information in the image, and has become an attractive approach to managing large medical image archives. In conventional CBIR systems for medical images, images are often segmented into regions which are used to derive two-dimensional visual features for region-based queries. Although such approach has the advantage of including only relevant regions in the formulation of a query, medical images that are inherently multidimensional can potentially benefit from the multidimensional feature extraction which could open up new opportunities in visual feature extraction and retrieval. In this study, we present a volume of interest (VOI) based content-based retrieval of four-dimensional (three spatial and one temporal) dynamic PET images. By segmenting the images into VOIs consisting of functionally similar voxels (e.g., a tumor structure), multidimensional visual and functional features were extracted and used as region-based query features. A prototype VOI-based functional image retrieval system (VOI-FIRS) has been designed to demonstrate the proposed multidimensional feature extraction and retrieval. Experimental results show that the proposed system allows for the retrieval of related images that constitute similar visual and functional VOI features, and can find potential applications in medical data management, such as to aid in education, diagnosis, and statistical analysis.     

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.     

Massive-scale image retrieval based on deep visual feature representation

This paper proposes an image retrieval algorithm towards massive-scale multimedia data. In order to be consistent with human visual system, we first design a color attention function to describe the important of different image patches. Subsequently, we combine color and texture to construct candidate regions, which will be fed into a deep neural network (DNN) for deep representation extraction. Then, we design a similarity function to calculate the distance among different images, where top-ranking images are considered as the required images. Experimental results show the effectiveness and robustness of our proposed method.     

DHSR: A Novel Semantic Retrieval Approach for Ubiquitous Multimedia

Semantic features are critical intelligence information for mobile ubiquitous multimedia, how to manage and retrieve the semantic information has been an important issue. In this paper, a novel semantic retrieval approach named Data Hiding based Semantic Retrieval (DHSR) for ubiquitous multimedia is proposed. This approach consists of the following features: (1) Every multimedia document has to be semantically annotated by several users before saved into multimedia database. (2) Semantic information described by object ontology will be hidden in the multimedia document data. (3) Semantic information will not be lost even if the multimedia document is copied, cut or leave the database. Our work provides a search engine with convenient user interfaces. The experimental results show that DHSR can search the multimedia documents reflecting users’ query intent more effectively compared with some traditional approaches.     

基于MPEG-7的多媒体内容描述方法研究

如何准确地描述多媒体信息是多媒体信息检索必须要解决的问题。文中介绍了MPEG-7标准多媒体内容的描述工具,然后从结构和内容两个方面研究MPEG-7描述多媒体内容的方法,并给出了实际的例子。