13183312
Wavelet based automatic thresholding for image segmentation
In this paper, a new systematic method to segment possible target areas based on wavelet transforms is presented. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayesian classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). We present examples which demonstrate the efficiency of the technique on a variety of targets.
14579845
Texture image retrieval based on a Gaussian mixture model and similarity measure using a Kullback divergence
In a content-based image retrieval (CBIR) system, indexing feature vectors and the similarity measure between feature vectors are two key factors for retrieval performance. We present a new CBIR system with statistical-model based image feature extraction in the wavelet domain and a Kullback divergence based similarity measure. A two component Gaussian mixture model (GMM) in the wavelet domain is employed and the model parameters are used to form features for image indexing. A new Kullback divergence based similarity measure is then presented for image retrieval. The experimental results demonstrate that the similarity measure based on the Kullback divergence is more effective than conventional similarity measures, such as the city-block distance and the Euclidean distance. It is shown that the new CBIR system, with the combination of the GMM and the new Kullback divergence based similarity measure, outperforms most other methods in retrieval performance for texture images, while keeping a comparable level of computational complexity.
1661896
Multiscale Fragile Watermarking Based on the Gaussian Mixture Model
In this paper, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) is presented. First, a GMM is developed to describe the statistical characteristics of images in the wavelet domain and an expectation-maximization algorithm is employed to identify GMM model parameters. With wavelet multiscale subspaces being divided into watermarking blocks, the GMM model parameters of different watermarking blocks are adjusted to form certain relationships, which are employed for the presented new fragile watermarking scheme for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. It is shown that the presented new method can securely embed a message bit stream, such as personal signatures or copyright logos, into a host image as fragile watermarks. Compared with conventional fragile watermark techniques, this new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. Meanwhile, with the embedded message bits spreading over the entire image area through the statistical model, the new method can detect and localize image tampering. Besides, the new multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as JPEG compression from malicious image attacks and, thus, can be used for semi-fragile watermarking
5541899
Automatic identification of digital video based on shot-level sequence matching
To locate a video clip in large collections is very important for retrieval applications, especially for digital rights management. In this paper, we present a novel technique for automatic identification of digital video. This new algorithm is based on dynamic programming that fully uses the temporal dimension to measure the similarity between two video sequences. A normalized chromaticity histogram is used as a feature which is illumination-invariant. Dynamic programming is applied on shot-level to find the optimal nonlinear mapping between video sequences. Two new normalized distance measures are presented for video sequence matching. One measure is based on the normalization of the optimal path found by dynamic programming. The other measure combines both the visual features and the temporal information. Experimental results show that the shot-level approach is robust to frame rate conversion, color correction, and compressions. The proposed distance measures are suitable for variable-length comparisons.
1464666
Feedforward neural networks with multilevel hidden neurons for remotely sensed image classification
Artificial neural network has been, used as a powerful tool for pattern classification. However, it is difficult to train when the data exhibit non-sparse or overlapping pattern classes which is often the case in practical applications. In this paper, we introduce the feedforward neural network with the hidden layer consisting of multilevel neurons. The convergence property of one-layer neural network with multilevel neurons is proved. The new feedforward model is inherently capable of fuzzy pattern classification of non-sparse or overlapping pattern classes. As an application, we apply the network for the classification of LANDSAT TM data. The results show that this approach produces better results compared with conventional neural networks.
44391380
Bi-directional gradient labeling and registration for gray-scale image segmentation
Watershed is one of the commonly used methods for image segmentation. In this paper, we introduce a new segmentation scheme based on bi-directional labeling and registration and prove that its segmentation performance is equivalent to that of conventional watershed but it is much more computational efficient The bi-directional labeling and registration scheme, which will be referred to as BIDS, involves only linear scans of image pixels. It uses one dimensional operations instead of queues while traditional segmentation algorithms are two dimensional problems. BIDS also provides unique labels for each homogeneous regions. In addition to achieving the same segmentation results as conventional watershed, BIDS is four times less computationally complex than the conventional watersheds by immersion.
1194012
BIG: A Resource-Bounded Information Gathering Agent
Effective information gathering on the WWW is a complex task requiring planning, scheduling, text processing, and interpretation-style reasoning about extracted data to resolve inconsistencies and to refine hypotheses about the data. This paper describes the rationale, architecture, and implementation of a next generation information gathering system - a system that integrates several areas of AI research under a single research umbrella. The goal of this system is to exploit the vast number of information sources available today on the NII including a growing number of digital libraries, independent news agencies, government agencies, as well as human experts providing a variety of services. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering coordination problem. Our solution is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource tradeoffs of different possible gathering approaches, extracts information from both unstructured and structured documents. and uses the extracted information to refine its search and processing activities.
12113641
Neighbor number, valley seeking and clustering
This paper proposes a novel nonparametric clustering algorithm capable of identifying shape-free clusters. This algorithm is based on a nonparametric estimation of the normalized density derivative (NDD) and the local convexity of the density distribution function, both of which are represented in a very concise form in terms of neighbor numbers. We use NDD to measure the dissimilarity between each pair of observations in a local neighborhood and to build a connectivity graph. Combined with the local convexity, this similarity measure can detect observations in local minima (valleys) of the density function, which separate observations in different major clusters. We demonstrate that this algorithm has a close relationship with the single-linkage hierarchical clustering and can be viewed as its extension. The performance of the algorithm is tested with both synthetic and real datasets. An example of color image segmentation is also given. Comparisons with several representative existing algorithms show that the proposed method can robustly identify major clusters even when there are complex configurations and/or large overlaps.
6359793
Integration of cluster ensemble and EM based text mining for microarray gene cluster identification and annotation
In this paper, we design and develop a unified system GE-Miner (Gene Expression Miner) to integrate cluster ensemble, text clustering and multi document summarization and provide an environment for comprehensive gene expression data analysis. We present a novel cluster ensemble approach to generate high quality gene cluster. In our text summarization module, given a gene cluster, our Expectation Maximization (EM) based algorithm can automatically identify subtopics and extract most probable terms for each topic. Then, the extracted top k topical terms from each subtopic are combined to form the biological explanation of each gene cluster. Experimental results demonstrate that our system can obtain high quality clusters and provide informative key terms for the gene clusters.
47233627
Integrative negotiation in complex organizational agent systems
This paper introduces an integrative negotiation mechanism, which enables agents to choose any attitude from the extremes of self-interested and fully cooperative to those that are partially self-interested and partially cooperative. Experimental work verifies this mechanism and explores the question whether it always improves the social welfare to have an agent be completely cooperative. It is found that it is good for the organization to have agents being partially cooperative in their local negotiation with other agents rather than being fully cooperative, in order to deal more effectively with the uncertainty of not having a more informed view of the state of the entire agent organization.
15047459
Disjoint Triangles of a Cubic Line Graph
Abstract.In this paper, we prove that a cubic line graph G on n vertices rather than the complete graph K4 has vertex-disjoint triangles and the vertex independence number . Moreover, the equitable chromatic number, acyclic chromatic number and bipartite density of G are respectively.