40273501
Pattern Discovery in Probabilistic Databases
Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don't work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.
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.
1498226
Approaches to implementing decision aggregation in multi-agent systems
8628384
Fingerprint Indexing Based on LAS Registration
Fingerprint indexing is an efficient technique that greatly improves the performance of fingerprint based person authentication systems by reducing the number of comparison. In this paper, we propose an indexing method based on fingerprint registration with a novel feature called local axial symmetry (LAS). The location and direction estimation of reference point are achieved in a straightforward way after the LAS field is achieved. Then the registered orientation field is utilized as a feature vector to perform the following indexing. A new scheme of the experiment is introduced and satisfactory experimental results are achieved on FVC2000 DB2 that the average search space is only 2.34% of all fingers in the condition of equal-sized training set and testing set.
603862
Video scene change detection method using unsupervised segmentation and object tracking
In order to manage the growing amount of video information efficiently, a video scene change detection method is necessary. Many advanced video applications such as video on demand (VOD) and digital library also require the scene change detection to organize the video content. In this paper, we present an effective scene change detection method using an unsupervised segmentation algorithm and the technique of object tracking based on the results of the segmentation. Our results have shown that this method can perform not only accurate scene change detection, but also obtain object level information of the video frames, which is very useful for video content indexing and analysis.
2688646
A unified framework for image database clustering and content-based retrieval
With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called <i>Markov Model Mediators</i> (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the summarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.
9156106
Blind Signal Separation of Similar Pitches and Instruments in a Noisy Polyphonic Domain
In our continuing work on ”Blind Signal Separation” this paper focuses on extending our previous work [1] by creating a data set that can successfully perform blind separation of polyphonic signals containing similar instruments playing similar notes in a noisy environment. Upon isolating and subtracting the dominant signal from a base signal containing varying types and amounts of noise, even though we purposefully excluded any identical matches in the dataset, the signal separation system successfully built a resulting foreign set of synthesized sounds that the classifier correctly recognized. Herein, this paper presents a system that classifies and separates two harmonic signals with added noise. This novel methodology incorporates Knowledge Discovery, MPEG7-based segmentation and Inverse Fourier Transforms.
11885669
A decision tree-based multimodal data mining framework for soccer goal detection
We propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by using combined multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is filtered by a pre-filtering step to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. Finally, the experimental results demonstrate the effectiveness of our framework for soccer goal extraction.