45740416
Automated Information Mining on Multimedia TV News Archives
This paper addresses an integrated information mining techniques for multimedia TV-news archive. The utilizes techniques from the fields of acoustic, image, and video analysis, for information retrieval on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast news video, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, the system can classify video into news versus commercials, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2003 to August of 2004. By applying speaker identification and/or image detection techniques, each news stories can be segmented with an accuracy of 96%. On screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related News contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodality query for specific news stories. Some experimental results are presented and discussed for the system reliability and performance evaluation and comparison.
35266077
VQ-Based Gray Watermark Hiding Scheme and Genetic Index Assignment
A vector quantisation (VQ) based watermarking scheme for hiding the gray watermark is presented. It expands the watermark size, and employs VQ index assignment procedure with genetic algorithm, called genetic index assignment (GIA), for watermarking. The gray watermark is coded by VQ, and obtained indices are translated into a binary bitstream with a much smaller size. We partition the codebook into two sub-codebooks, and use either one of them based on the value of the bit for embedding. Next, GIA is employed to find better imperceptibility of watermarked image. Experimental results show that the proposed method possesses advantages over other related methods in literature.
7106859
Vector quantization based on genetic simulated annealing
Genetic algorithm (GA) has been successfully applied to codebook design for vector quantization (VQ). However, most conventional GA-based codebook design methods need long runtime because candidate solutions must be "ne tuned by LBG. In this paper, a partition-based GA is applied to codebook design, which is referred to as genetic vector quantization (GVQ). In addition, simulated annealing (SA) algorithm is also used in GVQ to get more promising results and the corresponding method is referred to as GSAVQ. Both GVQ and GSAVQ use the linear scaling technique during the calculation of objective functions and use special crossover and mutation operations in order to obtain better codebooks in much shorter CPU time. Experimental results show that both of them save more than 71}87% CPU time compared to LBG. For di!erent codebook sizes, GVQ outperforms LBG by 1.1}2.1 dB in PSNR, and GSAVQ outperforms LBG by 1.2}2.2 dB in PSNR. In addition, GVQ and GSAVQ need a little longer CPU time than, the maximum decent (MD) algorithm, but they outperform MD by 0.2}0.5 dB in PSNR. 2001 Elsevier Science B.V. All rights reserved.
14443677
An Embedding Algorithm for Multiple Watermarks
A new algorithm for embedding several watermarks into the same original source is proposed in this paper. Instead of conventional watermark embedding algorithms that embed one watermark in one specific domain, we propose algorithms for embedding multiple watermarks into different compression domains to protect the ownership of the original images. The proposed schemes for embedding and extraction in different domains vary from the existing methods reported in the literature. Simulation results show their robustness for surviving intentional attacks and, hence, their effectiveness in protecting copyrights. They are also easy to implement.
13572740
Image Texture Segmentation with Ant Colony Systems
A new scheme for texture segmentation based on ant colony systems (ACS) is proposed in this paper. Texture segmentation is one of the important branches in image pattern recognition, which provides usefulness in many applications. Until now, how to find an effective way for accomplishing texture segmentation in practical applications is still a major task. In this paper, we employ wavelet coefficients and characteristics of different subbands to serve as the basis of characteristic vectors, and we use three feature-extraction elements, namely, the extrema, entropy, and energy, to compose the characteristic vector. To alleviate segmentation fragments caused from the information in high frequency bands of texture images, we integrate the fourth element, the mean variance, into the characteristic vector. Finally, we use ACS to find a trade-off between texture segmentation and fragments. Simulation results demonstrate the effectiveness and practicability of the proposed algorithm
18319018
A Multiple-Instance Neural Networks based Image Content Retrieval System
In this paper, we proposed a multiple-instance neural network (MINN) for content-based image retrieval (CBIR). In order to represent the rich content of an image without precisely image segmentation, the image retrieval problem is considered as a multiple-instance learning problem. A set of exemplar images are selected by a user, each of which is labelled as conceptual related (positive) or conceptual unrelated (negative) image. Then, the proposed MINN is trained by using the proposed learning algorithm to learn the user's preferred image concept from the positive and negative examples. Experimental results show that: (1) without image segmentation and using only the color histogram as the image feature, the MINN without relevance feedback performs slightly inferior to some leading image retrieval methods, and (2) the MINN with the relevance feedback can significantly improve the retrieving performance from 40.3% to 59.3%, which outperforms to the results of some leading image retrieval methods
10374803
Hemispherical Harmonic Surface Description and Applications to Medical Image Analysis
The use of surface harmonics for rigid and nonrigid shape description is well known. In this paper we define a set of complete hemispherical harmonic basis functions on a hemisphere domain and propose a novel parametric shape description method to efficiently and flexibly represent the surfaces of anatomical structures in medical images. As the first application of hemispherical harmonic theory in shape description, our technique differs from the previous surface harmonics shape descriptors, all of which don't work efficiently on the hemisphere-like objects that often exist in medical anatomical structures (e.g., ventricles, atriums, etc.). We demonstrate the effectiveness of our approach through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for surface modeling efficiency is described and the comparison results demonstrated that our method outperformed the previous approaches using spherical harmonic models.
17764116
Identifying transcriptional regulatory sites in the human genome using an integrated computation
Recently, biological databases and analytical methods have become available for analyzing gene expression and transcriptional regulatory sequences. However, users must make the complicated analyses to query the data in various databases, and to analyze the gene upstreams using various predictive tools. Beyond methods for predicting transcriptional regulatory sites, new automated and integrated methods for analyzing gene upstream sequences on a higher level are urgently required. We present an integrated system to predict transcriptional regulatory sites and detect co-occurrence of these regulatory sites after a set of genes are input. The system comprises a biological data warehousing system, pattern discovery programs, pattern occurrence association detectors and user interfaces. User profiles and history pages enable users to trace the sequence analyses for transcriptional regulatory sites.
1841883
Mining putative regulatory elements in gene promoter regions of Saccharomyces cerevisiae
The availability of genome-wide gene expression data provides a unique set of genes from which we can decipher the mechanisms underlying the common transcriptional response. Transcription factors, which can bind to specific DNA sites, cooperatively regulate the transcription of genes. This study attempts to mine putative binding sites to investigate how combinations of the sites predicted from known sites and over-represented repetitive elements are distributed in the promoter regions of groups of functionally related genes. The over-represented repetitive elements appearing in the associations are possible transcription factor binding sites. The deduced association rules would facilitate to predict putative regulatory elements and to identify genes which are potentially co-regulated by the putative regulatory elements. Our proposed approach is applied to Saccharomyces cerevisiae and the promoter regions of yeast ORFs.
205225795
ProClass protein family database
ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PROSITE patterns and PIR superfamilies. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of more than 120 000 sequence entries, approximately 60% of which is classified into about 3500 families. To maximize family information retrieval, the database provides links to various protein family/domain and structural class databases and contains multiple motif alignments of all PROSITE patterns as well as global alignments of PIR superfamilies. The motif sequences are retrieved from both PIR-International and SWISS-PROT databases, including a large number of new members detected by our GeneFIND family identification system. ProClass can be used to support full-scale genomic annotation, because of its high classification rate. The ProClass database is available for on-line search and record retrieval from our WWW server at http://diana.uthct.edu/proclass.html
7795322
The Protein Information Resource
The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.