6496414
Identifying Overlapped Objects for Video Indexing and Modeling in Multimedia Database Systems
The identification of the overlapped objects is a great challenge in object tracking and video data indexing. For this purpose, a backtrack-chain-updation split algorithm is proposed to assist an unsupervised video segmentation method called the "simultaneous partition and class parameter estimation" (SPCPE) algorithm to identify the overlapped objects in the video sequence. The backtrack-chain-updation split algorithm can identify the split segment (object) and use the information in the current frame to update the previous frames in a backtrack-chain manner. The split algorithm provides more accurate temporal and spatial information of the semantic objects so that the semantic objects can be indexed and modeled by multimedia input strings and the multimedia augmented transition network (MATN) model. The MATN model is based on the ATN model that has been used in artificial intelligence (AI) areas for natural language understanding systems, and its inputs are modeled by the multimedia input strings. In this paper, we will show that the SPCPE algorithm together with the backtrack-chain-updation split algorithm can significantly enhance the efficiency of spatio-temporal video indexing by improving the accuracy of multimedia database queries related to semantic objects.
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.
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.
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.
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.
9803776
Semantic propagation from relevance feedbacks
Relevance feedback has been a very useful tool to enhance the performance of content-based information retrieval (CBIR) systems. To fully make use of the precious user feedback provided to a system, we propose an approach named semantic propagation, which reveals the deep semantic relationships among objects in the database, given a set of relevance feedbacks between object pairs. In particular, we present two semantic propagation algorithms that are applicable to CIBR systems with a feature vector space model and a general metric space model, respectively. Experiments on a 3D model retrieval system and a logo image retrieval system are performed to show the effectiveness of the proposed methods.
1297870
An active learning framework for content-based information retrieval
We propose a general active learning framework for content-based information retrieval. We use this framework to guide hidden annotations in order to improve the retrieval performance. For each object in the database, we maintain a list of probabilities, each indicating the probability of this object having one of the attributes. During training, the learning algorithm samples objects in the database and presents them to the annotator to assign attributes. For each sampled object, each probability is set to be one or zero depending on whether or not the corresponding attribute is assigned by the annotator. For objects that have not been annotated, the learning algorithm estimates their probabilities with biased kernel regression. Knowledge gain is then defined to determine, among the objects that have not been annotated, which one the system is the most uncertain. The system then presents it as the next sample to the annotator to which it is assigned attributes. During retrieval, the list of probabilities works as a feature vector for us to calculate the semantic distance between two objects, or between the user query and an object in the database. The overall distance between two objects is determined by a weighted sum of the semantic distance and the low-level feature distance. The algorithm is tested on both synthetic databases and real databases of 3D models. In both cases, the retrieval performance of the system improves rapidly with the number of annotated samples. Furthermore, we show that active learning outperforms learning based on random sampling.
6417923
Analysis of Sound Features for Music Timbre Recognition
Recently, communication, digital music creation, and computer storage technology has led to the dynamic increasing of online music repositories in both number and size, where automatic content-based indexing is critical for users to identify possible favorite music pieces. Timbre recognition is one of the important subtasks for such an indexing purpose. Lots of research has been carried out in exploring new sound features to describe the characteristics of a musical sound. The moving picture expert group (MPEG) provides a standard set of multimedia features, including low level acoustical features based on latest research in this area. This paper introduces our newly designed temporal features used for automatic indexing of musical sounds and evaluates them with MPEG7 descriptors, and other popular features.
6431749
Reasoning with Multimedia Information Using Symbolic Projection
In many multimedia application systems, it is not the final goal to retrieve the relevant multimedia information from different multimedia information sources. Rather, post-processing of the retrieved multimedia information is needed. For example, the retrieved information is used as "known facts". The systems will do some reasoning to obtain further conclusions based on these multimedia form "known facts". We call this reasoning with multimedia information. Most current research work in multimedia information processing is focused on multimedia information retrieval, but post-processing the retrieved information is more or less ignored. This paper explores the way to tackle this problem by using symbolic projection. A case study of reasoning with still image information is presented. Some extensions to symbolic projection-introducing auxiliary pictorial objects in symbolic pictures that need to be processed-are discussed. We expect this paper will stimulate further research on this important but ignored topic.
45852164
An Agent-Based Hybrid Intelligent System for Financial Investment Planning
Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. however, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. this paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. this system currently consists of 13 different agents. the experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. the system is very flexible and robust. the success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.