45564986
Mining High-Level User Concepts with Multiple Instance Learning and Relevance Feedback for Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this book chapter, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role the user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested to show the feasibility and effectiveness of the proposed multimedia data mining framework.
5598037
Supporting Information Reuse for Video Data Indexing by Using Color Information
In this paper, an object tracking framework assisted by image segmentation and color indexing techniques to support information reuse for video data is proposed. In order to obtain more accurate results of object tracking, a color indexing method called Color Label Criteria (CLC) is adopted to relax some of the restrictions and assumptions in object tracking in our previous work with low additional cost. Our experiment results on a real video sequence show that the CLC color indexing method is very helpful to achieve more robust object tracking.
1252952
Information reuse and system integration in the development of a hurricane simulation system
This paper presents our effort in designing and implementing an advanced hurricane simulation system on the platform of the World Wide Web for the purpose of supporting decisional processes and hazard mitigation. The development of the system is based on amalgamated methodologies which come from diverse disciplines and is an integration of a variety of techniques. Superior to other similar systems in hurricane study, our system assembles and utilizes information and techniques in a more flexible and robust manner by taking advantage of system layering technique, component modeling strategy and database technologies. Preliminary experiments have been conducted to demonstrate the performance of the system.
5113927
An unsupervised segmentation framework for texture image queries
In this paper a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.
10875476
Affinity relation discovery in image database clustering and content-based retrieval
In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
9847925
A multimedia semantic model for RTSP-based multimedia presentation systems
An abstract multimedia semantic model called the multimedia augmented transition network (MATN) model is proposed to model the RTSP-based (real-time streaming protocol) multimedia presentation systems. RTSP provides a set of methods to support the functionality of "VCR-like" remote control on the client side so that users are allowed to control playback of the streaming media. The MATN model is used to model RTSP actions as well as temporal relations and synchronization control of the media streams. The advantages of using the MATN model are its simplicity, effectiveness, and ease of modification. Furthermore, a formal definition of the MATN model and modeling of RTSP actions using the MATN model are presented in detail.
11462933
On flows in bidirected graphs
Bouchet conjectured that every bidirected graph which admits a nowhere-zero bidirected flow will admit a nowhere-zero bidirected 6-flow [A. Bouchet, Nowhere-zero integer flows on a bidirected graph, J. Combin. Theory Ser. B 34 (1983) 279-292]. He proved that this conjecture is true with 6 replaced by 216. Zyka proved in his Ph.D dissertation that it is true with 6 replaced by 30. Khelladi proved it is true with 6 replaced with 18 for 4-connected graphs [A. Khelladi, Nowhere-zero integer chains and flows in bidirected graphs, J. Combin. Theory Ser. B 43 (1987) 95-115]. In this paper, we prove that Bouchet's conjecture is true for 6-edge connected bidirected graphs.
11698687
User term feedback in interactive text-based image retrieval
To alleviate the vocabulary problem, this paper investigates the role of user term feedback in interactive text-based image retrieval. Term feedback refers to the feedback from a user on specific terms regarding their relevance to a target image. Previous studies have indicated the effectiveness of term feedback in interactive text retrieval [14]. However, the term feedback has not shown to be effective in our experiments on text-based image retrieval. Our results indicate that, although term feedback has a positive effect by allowing users to identify more relevant terms, it also has a strong negative effect by providing more opportunities for users to specify irrelevant terms. To understand these different effects and their implications on the potential of term feedback, this paper further presents analysis of important factors that contribute to the utility of term feedback and discusses the outlook of term feedback in interactive text-based image retrieval.
7423428
Optimal Power Allocation in Wireless Networks with Transmitter-Receiver Power Tradeoffs
For many wireless communication links, such as those employing turbo codes or sequentially-decoded convolutional codes, the power consumption of the decoder at the receiver depends on the received signal power and, hence, on the transmitted signal power. By transmitting a signal at a power higher than the minimum required for successful reception, the transmitter can “assist” the receiver by reducing the receiver decoding effort and thereby reducing the overall power consumption on the link. In this paper, we consider sender-receiver power tradeoffs in data-gathering trees. We formulate an optimization problem to optimally assign power to the nodes in the tree for maximizing the lifetime of the data-gathering tree, which is equivalent to the time until network partition due to battery outage. We propose a Binary Search Algorithm for optimal power assignment among nodes that maximizes the tree lifetime. Our Binary Search Algorithm can be easily extended to handle practical considerations such as node mobility and peak power constraints at nodes. Using turbo codes as an example of a channel coding technique, we demonstrate significant improvements in network lifetimes as a result of sender-receiver power tradeoffs. These improvements are observed under a wide variety of network conditions and are more pronounced in densely deployed networks and networks with asymmetric power costs.
46639086
Triangle-Free Circuit Decompositions and Petersen Minor
In this paper, we characterize those simple graphs with no Petersen minor which admit triangle-free circuit decompositions.
6709890
An agent-based hybrid framework for database mining
While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.