Cluster-driven refinement for content-based digital image retrieval

Increasing application demands are pushing databases toward providing effective and efficient support for content-based retrieval over multimedia objects. In addition to adequate retrieval techniques, it is also important to enable some form of adaptation to users' specific needs. This paper introduces a new refinement method for retrieval based on the learning of the users' specific preferences. The proposed system indexes objects based on shape and groups them into a set of clusters, with each cluster represented by a prototype. Clustering constructs a taxonomy of objects by forming groups of closely-related objects. The proposed approach to learn the users' preferences is to refine corresponding clusters from objects provided by the users in the foreground, and to simultaneously adapt the database index in the background. Queries can be performed based solely on shape, or on a combination of shape with other features such as color. Our experimental results show that the system successfully adapts queries into databases with only a small amount of feedback from the users. The quality of the returned results is superior to that of a color-based query, and continues to improve with further use.     

QoS-based media retrieval for networked digital archives

When digital multimedia data are retrieved from distributed digital archives, Quality of Service (QoS) criteria can be used to enhance the user's usability. In this paper, assuming best-effort networks as the infrastructure, we describe a multimedia (MM) retrieval method based on the QoS concept for MM data archives in heterogeneous communication environments. We introduce a multi-level QoS model and use response time, which is calculated based on a system-level QoS criterion, as an application-level QoS criterion. The order of retrieving MM data is determined, based on both key-word matching using key words specified by the user and the application-level QoS criterion. A prototype system is developed for retrieving sightseeing-related MM data including video, images, voice, and text. The proposed method of data retrieval has a shorter response time than methods that do not take QoS criteria into account. Also, a search method based on key-word association is described that enables wide-area search and effective information finding.     

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.     

Context-sensitive queries for image retrieval in digital libraries

In this paper we show how to achieve a more effective Query By Example processing, by using active mechanisms of biological vision, such as saccadic eye movements and fixations. In particular, we discuss the way to generate two fixation sequences from a query image I _q and a test image I _t of the data set, respectively, and how to compare the two sequences in order to compute a similarity measure between the two images. Meanwhile, we show how the approach can be used to discover and represent the hidden semantic associations among images, in terms of categories, which in turn drive the query process.     

Machine annotation and retrieval for digital imagery of historical materials

Annotating digital imagery of historical materials for the purpose of computer-based retrieval is a labor-intensive task for many historians and digital collection managers. We have explored the possibilities of automated annotation and retrieval of images from collections of art and cultural images. In this paper, we introduce the application of the ALIP (Automatic Linguistic Indexing of Pictures) system, developed at Penn State, to the problem of machine-assisted annotation of images of historical materials. The ALIP system learns the expertise of a human annotator on the basis of a small collection of annotated representative images. The learned knowledge about the domain-specific concepts is stored as a dictionary of statistical models in a computer-based knowledge base. When an un-annotated image is presented to ALIP, the system computes the statistical likelihood of the image resembling each of the learned statistical models and the best concept is selected to annotate the image. Experimental results, obtained using the Emperor image collection of the Chinese Memory Net project, are reported and discussed. The system has been trained using subsets of images and metadata from the Emperor collection. Finally, we introduce an integration of wavelet-based annotation and wavelet-based progressive displaying of very high resolution copyright-protected images. A preliminary version of this work has been presented at the DELOS-NSF Workshop on Multimedia in Digital Libraries, Crete, Greece, June 2003. The work was completed when Kurt Grieb and Ya Zhang were students of The Pennsylvania State University. James Z. Wang and Jia Li are also affiliated with Department of Computer Science and Engineering, The Pennsylvania State University. Yixin Chen is also with the Research Institute for Children, Children's Hospital, New Orleans.     

数字电影发行相似案例快速检索方法

为解决传统相似案例检索方法在数字电影大型案例库中检索效率低下的问题,针对数字电影大型案例库进行了研究分析,从提高案例检索速度和参考价值的角度出发,提出了一种聚类、优选和匹配相结合的相似案例检索方法.通过聚类缩小案例的检索范围,通过优选在相应的检索范围内形成参考价值较高的候选案例集,通过匹配算法在候选案例集中寻找最相似案...     

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.     

Seoul digital complex as a strategy for building innovative cluster

In line with the new trend of the global economy, building innovative local clusters has become one of the core strategies to enhance economic development not only in the developed but also in the developing nations. Particularly the role and potential of localized innovation processes within clusters have been attracting considerable interests among scholars and policy makers alike. It is argued that the intensity and quality of competition is enhanced by the proximity of competitors in clusters. The paper argues how to identify the tasks and strategies necessary in order to build locally embedded innovative clusters.     

Data semantics for improving retrieval performance of digital newsvideo systems

We propose a novel four-step hybrid approach for retrieval and composition of video newscasts based on information contained in different metadata sets. In the first step, we use conventional retrieval techniques to isolate video segments from the data universe using segment metadata. In the second step, retrieved segments are clustered into potential news items using a dynamic technique sensitive to the information contained in the segments. In the third step, we apply a transitive search technique to increase the recall of the retrieval system. In the final step, we increase recall performance by identifying segments possessing creation-time relationships. A quantitative analysis of the performance of the process on a newscast composition shows an increase in recall by 59 percent over the conventional keyword-based search technique used in the first step     

An innovative digital method for the dynamic simulation of DC electromechanical systems

In this paper, an innovative digital simulation method, named `R-K-T' method, is presented. The new methodology combines Runge–Kutta and trapezoidal methods and possesses the advantages of both of them. The errors featuring the proposed method are analysed and their correction is worked out. As a case study, the circuit model of a small DC motor, acting as the engine starter of a road vehicle, is considered; the proposed methodology is applied to carry out the dynamic simulation of the electromechanical device. The results are obtained efficiently and with a good degree of accuracy; in particular, the numerical oscillations are suppressed.     

Research on digital characterization and identification process model of functional genes for intelligent innovative design

The inheritance and variation of functional knowledge is an important way to achieve product innovation. Analogous biological genetic inheritance can solve the problems with the inheritance and variation of functional knowledge. However, the different expressions of functional knowledge in different domains lead designers to consuming a lot of time for functional knowledge characterization and identification, which reduces the efficiency of functional knowledge application. Therefore, in order to solve the problem of inefficient application of functional knowledge in the genetic process caused by irregular expression of functional knowledge, the paper proposes a digital characterization and identification process model of functional genes for product intelligent innovative design. The process model enables product intelligent innovative design through digital characterization of functional genes and rapid identification of functional genes using computer assistance. The process model consists of four following steps. First, extract functional knowledge from patents and perform preliminary functional gene characterization. Second, characterize functional genes digitally by using computerized digital coding techniques. Third, identify the required functional genes by using image similarity matching algorithm. It assists designers to quickly identify the functional genes required for the target system. Fourth, obtain the target product design scheme. By “transcribing” and “translating” the required functional genes and using TRIZ tools to solve the translated scheme, the product innovative design scheme is finally obtained. The feasibility and effectiveness of the model are verified by the intelligent innovative design of shared bicycle parking management device.     

An intelligent content-based image retrieval methodology using transfer learning for digital IP protection

Trademarks are used by companies to help customers identify products or services using images or logos in addition to slogans, words, names, sounds, smells, color, and motions. Trademark logos are widely distributed through advertising and published through online media websites and social networks such as Facebook, Pinterest, and Flicker. The intellectual property (IP) rights of the trademark owners have strong legal protection when registered with international intellectual property platforms such as the US Patent and Trademark Office and the World Intellectual Property Office. Using a registered trademark without prior consent of the owner may result in intellectual property infringement with severe legal consequences under civil or criminal law. Companies invest large capital resources in protecting their trademark from being copied or misused in ways that confuse the customers or steal market share. This research focuses on trademark (TM) logo image retrieval systems used in the cyber marketplaces to identify similar TM logo images online automatically and intelligently. The methodology developed for TM logo similarity measurement is based on content-based image retrieval. Content retrieval reduces the gap between high-level semantic interpretation of human vision and the low-level features processed by the machine. The proposed transfer learning methodology uses embedded learning with triplet loss to fine-tune a pre-trained convolutional neural network model. The Logo-2K+ large-scale logo dataset is re-organized and divided into the top 70% as the training set and the remaining 30% as the testing set. The results show that the novel transfer learning approach is developed and demonstrated in this research for the intelligent automatic detection of similar TM logo images with high accuracy. The verification experiments (trained with 7625 logos and tested with 3221 logos) demonstrates that the Recall@10 of the test set can reach 95% using the advanced convolutional neural network model (VGG19) adjusted with the novel transfer learning methodology.     

Distinguishable zero-watermarking scheme with similarity-based retrieval for digital rights Management of Fundus Image

Multimedia Tools and Applications - Zero-watermarking scheme can provide durable and distortion-free digital rights management (DRM) for fundus image which plays an important role in diagnosis of...     

基于中介器/包装器的联合数字图书馆集成信息检索机制

联合数字图书馆的目的就是为了实现信息的集成共享,给用户提供统一资源查询服务。提出了一种基于中介器 /包装器的联合数字图书集成信息检索机制,以RDF作为中间信息表示语言,完成查询转换和结果合成。此外描述了实现此机制的FDL集成信息检索系统,重点介绍了基于RDF的中介器、包装器产生模块的设计。并以原型系统为例,测试分析了系统性能。     

Design and evaluation of systems to support interaction capture and retrieval

Although many recent systems have been built to support Information Capture and Retrieval (ICR), these have not generally been successful. This paper presents studies that evaluate two different hypotheses for this failure, firstly that systems fail to address user needs and secondly that they provide only rudimentary support for ICR. Having first presented a taxonomy of different systems built to support ICR, we then describe a study that attempts to identify user needs for ICR. On the basis of that study we carried out two user-oriented evaluations. In the first, we carried out a task-based evaluation of a state-of-the-art ICR system, finding that it failed to provide users with abstract ways to view meetings data, and did not present users with information categories that they considered to be important. In a second study, we introduce a new method for comparative evaluation of different techniques for accessing meetings data. The second study showed that simple interface techniques that extracted key information from meetings were effective in allowing users to extract gist from meetings data. We conclude with a discussion of outstanding issues and future directions for ICR research.     

嵌入式数字视频监控系统通用存储与检索方案

为了解决现有的嵌入式数字视频监控系统的多路视频存储与检索的结构差异,从而严重制约视频监控系统的可复用性与引起系统I/O瓶颈问题,提出一种嵌入式数字视频监控系统通用存储与检索方案.采用组件化思想构建视频存储与视频检索模块,通过设计存储调度算法减少了I/O瓶颈问题,使用检索控制策略降低了检索拥塞.与以前的系统相比在支持多路视频存储与检索方面表现出改进的性能,同时具有更好的可重用性.     

An integrated model of interaction experience for information retrieval in a Web-based encyclopaedia

An experiment, using two versions of a Web site varying in usability, tested three models of user experience: an interaction experience model, a technology acceptance model and an integrated experience-acceptance model. We found that the perceptions of three product attributes (Pragmatic Quality, Hedonic Quality-stimulation and Hedonic Quality-identification) and technology acceptance variables (the beliefs of Perceived Ease of Use, Perceived Enjoyment and Perceived Usefulness, and Intention to Use) are separate underlying psychological dimensions. A positive effect of usability on task performance, interaction experience and acceptance was found. In the interaction experience model, the evaluation of Goodness (overall interaction quality) was less stable and influenced by both Pragmatic Quality and Hedonic Quality, but the evaluation of Beauty was more stable and only influenced by Hedonic Quality. In the technology acceptance model, Perceived Ease of Use was a determinant of Perceived Enjoyment and Perceived Usefulness, and the latter two were independent determinants of Intention to Use. In the integrated model, perceptions of product attributes were independent determinants of beliefs, but evaluations were not independent determinants of Intention to Use. Future modelling work should address a range of interactive systems, information architecture and individual differences.