29695672
Managing and Mining Multimedia Data, Animations and Annotations
Several advances have been made on managing multimedia data stores. We now have products for multimedia data management. This article provides an overview of managing multimedia databases and describes techniques for managing multimedia animations and annotations as well as mining multimedia data. 
 
 
Keywords: 
 
data mining; 
mulitimedia databases; 
animations; 
images; 
multimedia database; 
management system
46376386
Improving Image Annotations Using WordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
15085728
Object Detection for Hierarchical Image Classification
Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval in the image domain is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring that relevant information is not overlooked (high recall). The unstructured format of images tends to resist the deployment of standard search mechanism and classification techniques. As a method to provide better organization of images, clustering is important aspect for effective image retrieval. For this, we need to identify objects that appear in images. In this paper, we propose using an automatic scalable object boundary detection algorithm based on edge detection and region growing techniques to accurately identify all object boundaries that appear in images, and an efficient merging algorithm for joining adjacent regions which uses an adjacency graph to avoid the over-segmentation of regions. To illustrate the effectiveness of our algorithm in automatic image classification we implement a very basic system and observe how well our approach works when objects in images have varying degrees of complex organization along with shading and highlights.
17047961
Image annotations by combining multiple evidence & wordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
9818080
Automatic image annotation and retrieval using weighted feature selection
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.
15759515
An Effective Evidence Theory Based K-Nearest Neighbor (KNN) Classification
In this paper, we study various K nearest neighbor (KNN) algorithms and present a new KNN algorithm based on evidence theory. We introduce global frequency estimation of prior probability (GE) and local frequency estimation of prior probability (LE). A GE for a class is the prior probability of the class across the whole training data space based on frequency estimation; on the other hand, a LE for a class in a particular neighborhood is the prior probability of the class in this neighborhood space based on frequency estimation. By considering the difference between the GE and the LE of each class, we present a solution to the imbalanced data problem in some degree without doing re-sampling. We compare our algorithm with other KNN algorithms using two benchmark datasets. Results show that our KNN algorithm outperforms other KNN algorithms, including basic evidence based KNN.
29608911
Password Recovery on Challenge and Response: Impossible Differential Attack on Hash Function
We propose practical password recovery attacks against two challenge-response authentication protocols using MD4. When a response is computed as MD4(Password||Challenge), passwords up to 12 characters are practically recovered. To recover up to 8 characters, we need 16 times the amount of eavesdropping and 16 times the number of queries, and the off-line complexity is less than 235 MD4 computations. To recover up to 12 characters, we need 210 times the amount of eavesdropping and 210 times the number of queries, and the off-line complexity is less than 240 MD4 computations.When a response is computed as MD4(Password||Challenge||Password), passwords up to 8 characters are practically recovered by 28 times the amount of eavesdropping and 28 times the number of queries, and the off-line complexity is less than 239 MD4 computations. Our approach is similar to the "Impossible differential attack", which was originally proposed for recovering the block cipher key. Good impossible differentials for hash functions are achieved by using local collision. This indicates that the presence of one practical local collision can damage the security of protocols.
14258683
A framework for checking behavioral compatibility for component selection
Component selection and composition are the main issues in component-based development (CBD). Existing approaches suffer from the problem that the components retrieved usually do not exactly fit with other components in the system being developed. While formal methods can be used to describe and check semantic characteristics to better match components, there are practical limitations which restrict their adoption. In this paper, we propose a framework to support a semantic description and selection of components. Towards this we first introduce a Simple Component Interface Language (SCIL). SCIL files can be translated to a variety of models including those that have a formal basis. We report our experience with two specific tools, viz., Reactive Modules and Alloy with a view to using tools based on formal methods but without exposing the details of the tools
17681650
Cross-Layer Optimized Data Gathering in Wireless Multimedia Sensor Networks
Using multimedia sensor nodes can enhance the capability of wireless sensor networks (WSNs) for event description. In some scenarios, e.g., an erupting volcano, the WSNs are not deployed to work for long time. Instead, the WSNs aim to deliver continuous and reliable multimedia data as much as possible within an expected lifetime. In this paper, we focus on the efficient multimedia data gathering in WSNs within an expected lifetime. A distributed transmission Radius and data generation Rate Adjustment (RRA) scheme is proposed based on a cross-layer design, considering the interaction among physical, network and transport layers. Simulation results show that the proposed RRA strategy can effectively enhance the data gathering performance in wireless multimedia sensor networks (WMSNs) by dynamically adjusting the transmission radius of sensor nodes and the data generation rate of source nodes.
15780361
The Real-Time Networked Data Gathering Systems Based on EtherCAT
Distributed control systems have been growing explosively and the Fieldbus technology has been widely employed for networked data gathering system. However, few Field-bus is real time down to the I(Input)/O(Output) level and most of them have a large number of sub-systems. In this paper we introduce a novel high-speed industry Ethernet bus known as EtherCAT into data gathering system. A networked data gathering system based on EtherCAT is achieved and the experimental results indicate the system can perform well in real-time data transmission.
33314662
Design of error-tolerant cache memory for multithreaded computing
With the trend towards nanometer billion-transistor integration, reliable computing becomes increasingly challenged by semiconductor process variations and low-level physical effects. This is particularly a problem for on-chip memory circuits. In this paper, we propose an error-tolerant memory design technique based on a unique phenomenon referred to as the inter-thread transient redundancy in multithreaded computing. A new memory microarchitecture is developed that exploits dynamic mapping strategies for compensation of unpredictable performance variations and soft errors. Trace driven simulations on the SPEC CPU2000 benchmarks show the advantages of the proposed technique for improving error tolerance in multithreaded microprocessors.