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A FAST SEARCH METHOD FOR DNA SEQUENCE DATABASE USING HISTOGRAM INFORMATION
QIU CHEN,KOJI KOTANI,FEIFEI LEE,TADAHIRO OHMI
International Journal of Bioinformatics Research , 2011,
Abstract: DNA sequence search is a fundamental topic in bioinformatics. The Smith-Waterman algorithmachieved highest accuracy among various sequence alignment tools, but it usually spends much computationaltime to search on large DNA sequence database. On the contrary, BLAST and FASTA have improved the searchspeed by using heuristic approaches, by there is a possibility of missing an alignment or giving inaccurate output.This paper presents an efficient hierarchical method to improve the search speed while the accurate is being keptconstant. For a given query sequence, firstly, a fast histogram based method is used to scan the sequences in thedatabase. A large number of DNA sequences with low similarity will be excluded for latter searching. The Smith-Waterman algorithm is then applied to each remainder sequences. Experimental results show the proposedmethod combining histogram information and Smith-Waterman algorithm is a more efficient algorithm for DNAsequence search
Fast Search for MPEG Video Clips from Large Video Database Using Combined Histogram Features
Feifei Lee,Koji Kotani,Qiu Chen,Tadahiro Ohmi
Lecture Notes in Engineering and Computer Science , 2010,
Abstract:
Fast Subsequent Color Iris Matching in large Database  [PDF]
Adnan Alam Khan,Safeeullah Soomro,Irfan Hyder
International Journal of Computer Science Issues , 2012,
Abstract: Databases play an important role in cyber world. It provides authenticity across the globe to the legitimate user. Biometrics is another important tool which recognizes humans using their physical statistics. Biometrics system requires speedy recognition that provides instant and accurate results. Biometric industry is looking for a new algorithm that interacts with biometric system reduces its recognition time while searching its record in large database. We propose a method which provides an appropriate solution for the aforementioned problem. Iris images database could be smart if iris image histogram ratio is used as its primary key. So, we have developed an algorithm that converts image histogram into eight byte code which will be used as primary key of a large database. Second part of this study explains how color iris image recognition can take place. For this a new and efficient algorithm is developed that segments the iris image and performs recognition in much less time. Our research proposes a fast and efficient algorithm that recognizes color irises from large database. We have already implemented this algorithm in Matlab. It provides real-time, high confidence recognition of a person's identity using mathematical analysis of the random patterns that are visible within the iris of an eye.
Fast Subsequent Color Iris Matching in large Database  [PDF]
Adnan Alam Khan,Safeeullah Soomro,Irfan Hyder
Computer Science , 2012,
Abstract: Databases play an important role in cyber world. It provides authenticity across the globe to the legitimate user. Biometrics is another important tool which recognizes humans using their physical statistics. Biometrics system requires speedy recognition that provides instant and accurate results. Biometric industry is looking for a new algorithm that interacts with biometric system reduces its recognition time while searching its record in large database. We propose a method which provides an appropriate solution for the aforementioned problem. Iris images database could be smart if iris image histogram ratio is used as its primary key. So, we have developed an algorithm that converts image histogram into eight byte code which will be used as primary key of a large database. Second part of this study explains how color iris image recognition can take place. For this a new and efficient algorithm is developed that segments the iris image and performs recognition in much less time. Our research proposes a fast and efficient algorithm that recognizes color irises from large database. We have already implemented this algorithm in Matlab. It provides real-time, high confidence recognition of a person's identity using mathematical analysis of the random patterns that are visible within the iris of an eye.
A Modification of Grover's Algorithm as a Fast Database Search  [PDF]
D. A. Ross
Physics , 1998,
Abstract: A modification of Grover's algorithm is proposed, which can be used directly as a fast database search. An explicit two q-bit example is displayed in detail. We discuss the case where the database has multiple entries corresponding to the same target value.
Historic Chinese Architectures Image Retrieval by SVM and Pyramid Histogram of Oriented Gradients Features
Bailing Zhang,Yonghua Song,Sheng-uei Guan,Yanchun Zhang
International Journal of Soft Computing , 2012, DOI: 10.3923/ijscomp.2010.19.28
Abstract: Content-Based Image Retrieval (CBIR) of historic Chinese architecture images is an important area of research bridging society, culture and information technology. Most of the image features used in previous content-based image retrieval systems such as colour, texture and some simple shape descriptors are not effective in describing building images due to high variability in the heterogeneous architectural image collections. This study investigates content-based architectural image retrieval mainly by shape features. The recently proposed shape descriptor, Pyramid Histogram of Oriented Gradients (PHOG) features, counts occurrences of gradient orientation in localized portions of an image and has been proved as an efficient tool for providing spatial distribution of edges. Many existing image retrieval systems attempt to compare the query image with every target image in the database to find the top matching images, resulting in an essentially linear search which is prohibitive when the database is large. To solve the problem, it propose to introduce classification as the first stage in the retrieval system. Based on the PHOG features, it apply the Support Vector Machine (SVM) to automatically classify the ancient Chinese architecture images. Cross-validation test results indicate that the generalization performance of the SVM was over 60% compared to neural network's accuracy of 30% and kNN's accuracy 50%.
Protein sequence database for pathogenic arenaviruses  [cached]
Bui Huynh-Hoa,Botten Jason,Fusseder Nicolas,Pasquetto Valerie
Immunome Research , 2007, DOI: 10.1186/1745-7580-3-1
Abstract: Background Arenaviruses are a family of rodent-borne viruses that cause several hemorrhagic fevers. These diseases can be devastating and are often lethal. Herein, to aid in the design and development of diagnostics, treatments and vaccines for arenavirus infections, we have developed a database containing protein sequences from the seven pathogenic arenaviruses (Junin, Guanarito, Sabia, Machupo, Whitewater Arroyo, Lassa and LCMV). Results The database currently contains a non-redundant set of 333 protein sequences which were manually annotated. All entries were linked to NCBI and cited PubMed references. The database has a convenient query interface including BLAST search. Sequence variability analyses were also performed and the results are hosted in the database. Conclusion The database is available at http://epitope.liai.org:8080/projects/arena and can be used to aid in studies that require proteomic information from pathogenic arenaviruses.
Local Features Based Image Sequence Retrieval  [cached]
Xiang Fu,Jie-xian Zeng
Journal of Computers , 2010, DOI: 10.4304/jcp.5.7.987-994
Abstract: We propose an approach to retrieve image sequences similar to the given query image sequence from database. Our proposed approach consists of three phases. First, the query image sequence and every database image sequence are segmented into several shots based on histogram difference between local images of consecutive frames. Second, for each shot, one or more key frames are selected based on histogram difference between local images of benchmark frame and each followed frame. Third, to retrieve image sequences similar to the given query image sequence, similarity between the key frames of query image sequence shot and key frames of each database image sequence is computed. The similarity is also measured using the histogram difference between local images of key frames. Local image is defined by interest points, that is, the regions around all interest points for a frame. The database video shots with similarity higher than a predefined threshold are output and returned to the user. Experimental results show that the proposed video shot detection method can overcome the deficiency of traditional histogram-based method that different contents frames have similar histograms, can distinguish between gradual changes and camera motions effectively, and can effectively detect both abrupt transitions and gradual transitions; the key frames selected by the proposed method have good representative power and can improve the performance of video shot retrieval.
Fast-Find: A novel computational approach to analyzing combinatorial motifs
Micah Hamady, Erin Peden, Rob Knight, Ravinder Singh
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-1
Abstract: Here we present a new approach, Fast-FIND (Fast-Fully Indexed Nucleotide Database), that uses a relational database to support rapid indexed searches for arbitrary combinations of patterns defined either by sequence or composition. Fast-FIND is easy to implement, takes less than a second to search the entire Drosophila genome sequence for arbitrary patterns adjacent to sites of alternative polyadenylation, and is sufficiently fast to allow sensitivity analysis on the patterns. We have applied this approach to identify transcripts that contain combinations of sequence motifs for RNA-binding proteins that may regulate alternative polyadenylation.Fast-FIND provides an efficient way to identify transcripts that are potentially regulated via alternative polyadenylation. We have used it to generate hypotheses about interactions between specific polyadenylation factors, which we will test experimentally.DNA- and RNA-binding proteins are essential for the regulation of gene expression at many levels. They control many biological processes in all organisms by altering gene expression at the levels of transcription, pre-mRNA splicing, mRNA export, stability, localization, and translation. Although some proteins bind specific sequences, others bind short or degenerate patterns, also called motifs, that occur frequently in the genome by chance. These patterns can even be defined by base composition rather than by an exact sequence.Proteins that bind frequently-occurring sites cannot individually be highly specific, but such proteins can achieve specificity by cooperation in complexes clustered near regulatory sequences. This combinatorial control is the rule rather than the exception in higher eukaryotes for critical processes including transcription [1] and splicing [2], and has also been observed in bacterial transcription [3]. Building up regulatory complexes in this way, rather than using individual gene- or transcript-specific factors, confers many advantages. These advant
A Local Search Method Using Histogram Features for Fast Retrieval of DNA Sequences
Qiu Chen,Koji Kotani,Feifei Lee,Tadahiro Ohmi
Lecture Notes in Engineering and Computer Science , 2010,
Abstract:
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