11815466
A Wavelet Based Image Watermarking Scheme
A novel robust watermarking scheme based on wavelet transform is presented in this paper. The watermark is embedded into significant triplet of wavelet coefficients in the three lowest detail subband images. The embedding algorithm only needs to modify a small amount of the wavelet coefficients, which leads to very small distortion and guarantees the visual quality of the watermarked image. The performance of the proposed algorithm is tested on a series of normal attacks in image processing, such as JPEG compression, Gaussian noise addition, median filter, etc. The corresponding experimental results show that the proposed watermarking scheme has better performance.
20577453
A Novel Clustering Method Based on SVM
For the problem of cluster analysis, the objective function based algorithms are popular and widely used methods. However, the performance of these algorithms depends upon the priori information about cluster number and cluster prototypes. Moreover, it is only effective for analyzing data set with the same type of cluster prototypes. For this end, this paper presents a novel algorithm based on support vector machine (SVM) for realizing fully unsupervised clustering. The experimental results with various test data sets illustrate the effectiveness of the proposed novel clustering algorithm based on SVM.
28211985
A Fully Unsupervised Image Segmentation Algorithm Based on Wavelet-Domain Hidden Markov Tree Models
A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.
36004220
A Video Shot Boundary Detection Algorithm Based on Feature Tracking
Partitioning a video sequence into shots is the first and key step toward video-content analysis and content-based video browsing and retrieval. A novel video shot boundary detection algorithm is presented based on the feature tracking. First, the proposed algorithm extracts a set of corner-points as features from the first frame of a shot. Then, based on the Kalman filtering, these features are tracked with windows matching method from the subsequent frames. According to the characteristic pattern of pixels intensity changing between corresponding windows, the measure of shot boundary detection can be obtained to confirm the types of transitions and the time interval of gradual transitions. The experimental results illustrate that the proposed algorithm is effective and robust with low computational complexity
35059796
A Unified Framework for Shot Boundary Detection
According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash.
14087664
Maximum entropy fuzzy clustering with application to real-time target tracking
The problem of data association for target tracking in a cluttered environment is discussed. In order to deal with the problem of data association for real time target tracking, a novel data association method based on maximum entropy fuzzy clustering is proposed. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probabilities are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. At the same time, in order to deal with the uncertainty of the measurements, a new weight assignment is introduced. Moreover, the characteristic of the discrimination factor is analyzed, and the influence of the clutter density on it is also considered, which enables the algorithm eliminate those invalidate measurements and reduce the computational load. Finally, the simulation results show that the proposed algorithms have advantages over the existing ones in terms of efficiency and low computational load.
40362881
A Knowledge Learning Approach to Information Extraction from Multiple Text Based Web Sites
This paper describes a learning/adaptive approach to automatically building knowledge bases for information extraction from text based web pages. A frame based representation is introduced to represent domain knowledge as knowledge unit frames. A frame learning algorithm is developed to automatically learn knowledge unit frames from training examples. Some training examples can be obtained by automatically parsing a number of tabular web pages in the same domain, which greatly reduced the amount of time consuming manual work. This approach was investigated on ten web sites of real estate advertisements and car advertisements and nearly all the information was successfully extracted with very few false alarms. These results suggest that both the knowledge unit frame representation and the frame learning algorithm work well, domain specific knowledge bases can be learned from training examples, and the domain specific knowledge base can be used for information extraction from flexible text-based semi-structured Web pages on multiple Web sites. The investigation of the knowledge representation on five other domains suggests that this approach can be easily applied to other domains by simply changing the training examples.
7220368
Quantification and characterisation of arteries in retinal images
A computerised system is presented for the automatic quantification of blood vessel topography in retinal images. This system utilises digital image processing techniques to provide more reliable and comprehensive information for the retinal vascular network. It applies strategies and algorithms for measuring vascular trees and includes methods for locating the centre of a bifurcation, detecting vessel branches, estimating vessel diameter, and calculating angular geometry at a bifurcation. The performance of the system is studied by comparison with manual measurements and by comparing measurements between red-free images and fluorescein images. In general an acceptable degree of accuracy and precision was seen for all measurements, although the system had difficulty dealing with very noisy images and small or especially tortuous blood vessels.
17022495
Improving Web clustering by cluster selection
Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper's experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
40240913
Combining Contents and Citations for Scientific Document Classification
This paper introduces a classification system that exploits the content information as well as citation structure for scientific paper classification. The system first applies a content-based statistical classification method which is similar to general text classification. We investigate several classification methods including K-nearest neighbours, nearest centroid, naive Bayes and decision trees. Among those methods, the K-nearest neighbours is found to outperform others while the rest perform comparably. Using phrases in addition to words and a good feature selection strategy such as information gain can improve system accuracy and reduce training time in comparison with using words only. To combine citation links for classification, the system proposes an iterative method to update the labellings of classified instances using citation links. Our results show that, combining contents and citations significantly improves the system performance.
2838896
SplitTester : software to identify domains responsible for functional divergence in protein family
BackgroundMany protein families have undergone functional divergence after gene duplications such that current subgroups of the family carry out overlapping but distinct biological roles. For the protein families with known functional subtypes (a functional split), we developed the software, SplitTester, to identify potential regions that are responsible for the observed distinct functional subtypes within the same protein family.ResultsOur software, SplitTester, takes a multiple protein sequences alignment as input, generated from protein members of two subgroups with known functional divergence. SplitTester was designed to construct the neighbor joining tree (a split cluster) from variable-sized sliding windows across the alignment in a process called split-clustering. SplitTester identifies the regions, whose split cluster is consistent with the functional split, but may be inconsistent with the phylogeny of the protein family. We hypothesize that at least some number of these identified regions, which are not following a random mutation process, are responsible for the observed functional split. To test our method, we used reverse transcriptase from a group of Pseudoviridae retrotransposons: to identify residues specific for diverged primer recognition. Candidate regions were then mapped onto the three dimensional structures of reverse transcriptase. The locations of these amino acids within the enzyme are consistent with their biological roles.ConclusionSplitTester aims to identify specific domain sequences responsible for functional divergence of subgroups within a protein family. From the analysis of retroelements reverse transcriptase family, we successfully identified the regions splitting this family according to the primer specificity, implying their functions in the specific primer selection.