Results 1 - 10 of 3,433

Muscle: multiple sequence alignment with high accuracy and high throughput

by Robert C. Edgar - NUCLEIC ACIDS RES , 2004
"... We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the logexpectation score, and refinement using tree-dependent r ..."
Abstract - Cited by 2509 (7 self) - Add to MetaCart
-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement

Optimizing Search Engines using Clickthrough Data

by Thorsten Joachims , 2002
"... This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches ..."
Abstract - Cited by 1314 (23 self) - Add to MetaCart
-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a

On-line selection of discriminative tracking features

by Robert T. Collins, Yanxi Liu, Marius Leordeanu , 2003
"... This paper presents an on-line feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for track-ing the ..."
Abstract - Cited by 356 (5 self) - Add to MetaCart
according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adap-tively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts

Hamming embedding and weak geometric consistency for large scale image search

by Herve Jegou, Matthijs Douze, Cordelia Schmid - In ECCV , 2008
"... Abstract. This paper improves recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We, first, analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the suboptimality of such a representation for ..."
Abstract - Cited by 330 (35 self) - Add to MetaCart
Abstract. This paper improves recent methods for large scale image search. State-of-the-art methods build on the bag-of-features image representation. We, first, analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the suboptimality of such a representation

Adaptive document image binarization

by J. Sauvola, M. Pietikäinen - PATTERN RECOGNITION , 2000
"... A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type-related degradations are addressed. Two new algorithms are applied t ..."
Abstract - Cited by 213 (0 self) - Add to MetaCart
A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type-related degradations are addressed. Two new algorithms are applied

A Survey of Evolution Strategies

by Thomas Bäck, Frank Hoffmeister, Hans-Paul Schwefel - Proceedings of the Fourth International Conference on Genetic Algorithms , 1991
"... Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the first mutation--selection scheme to the refined (¯,)--ES including the gen ..."
Abstract - Cited by 263 (3 self) - Add to MetaCart
Similar to Genetic Algorithms, Evolution Strategies (ESs) are algorithms which imitate the principles of natural evolution as a method to solve parameter optimization problems. The development of Evolution Strategies from the first mutation--selection scheme to the refined (¯,)--ES including

Dynamic Real-Time Deformations using Space Time Adaptive Sampling

by Gilles Debunne, Mathieu Desbrun , Marie-Paule Cani, Alan H. Barr , 2001
"... This paper presents a robust, adaptive method for animating dynamic visco-elastic deformable objects that provides a guaranteed frame rate. Our approach uses a novel automatic space and time adaptive level of detail technique, in combination with a largedisplacement (Green) strain tensor formulation ..."
Abstract - Cited by 226 (14 self) - Add to MetaCart
This paper presents a robust, adaptive method for animating dynamic visco-elastic deformable objects that provides a guaranteed frame rate. Our approach uses a novel automatic space and time adaptive level of detail technique, in combination with a largedisplacement (Green) strain tensor

Adapting ranking SVM to document retrieval

by Yunbo Cao, Jun Xu, Tie-yan Liu, Hang Li, Yalou Huang, Hsiao-wuen Hon - In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 2006
"... The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a “learning to rank” method, to document retrieval. First, correctly ranki ..."
Abstract - Cited by 124 (21 self) - Add to MetaCart
The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a “learning to rank” method, to document retrieval. First, correctly

Summarizing Text Documents: Sentence Selection and Evaluation Metrics

by Jade Goldstein, Mark Kantrowitz, Vibhu Mittal, Jaime Carbonell - In Research and Development in Information Retrieval , 1999
"... Human-quality text summarization systems are difficult to design, and even more difficult to evaluate, in part because documents can differ along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for incl ..."
Abstract - Cited by 236 (7 self) - Add to MetaCart
for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted combination of statistical and linguistic features. The statistical features were adapted from standard IR methods

Active Shape Models -- "Smart Snakes"

by T. F. Cootes, C. J. Taylor - IN PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE , 1992
"... We describe 'Active Shape Models' which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets of labe ..."
Abstract - Cited by 218 (4 self) - Add to MetaCart
We describe 'Active Shape Models' which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples. These models, known as Point Distribution Models, represent objects as sets
Results 1 - 10 of 3,433