18984298
Block Standstill and Homogeneity Based Fast Motion Estimation Algorithm for H.264 Video Coding
The paper proposed a fast motion estimation algorithm based on the spatial homogeneity and temporal standstill of video sequences. It classified a frame into different regions such as standstill, stationary or dynamic region. For MBs lying in the standstill region, motion estimation is skipped; for MBs lying in the stationary region, motion estimation is applied once to an MB in the region, and the resulting motion vector will be used by other MBs in the same region. Normal motion estimation is only carried out in the dynamic region. The new algorithm can be used either by itself alone, or by combining with other existing fast motion estimation methods. Experimental results show that the new algorithm can significantly improve the time efficiency of the H.264 encoder, and is able to achieve an average reduction of 27% in encoding, with an average PSNR loss of only 0.09 dB and 0.42% bit rate increase, compared with the original H.264 reference software.
981043
Learning Event Durations from Event Descriptions
We have constructed a corpus of news articles in which events are annotated for estimated bounds on their duration. Here we describe a method for measuring inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data yield coarse-grained event duration information, considerably outperforming a baseline and approaching human performance.
39269573
An adaptive edge-preserving artifacts removal filter for video post-processing
We propose an adaptive edge-preserving artifacts removal filter (EPARF) for post-processing video compressed using block-based motion compensation so as to improve visual quality. The proposed filter is based on half-quadratic regularization, and it can adaptively remove both blocking and ringing artifacts while preserving object edges. The proposed algorithm has been applied to video sequences compressed by the MSRA scalable video coding (SVC) codec with block-based motion-compensated temporal filtering (MCTF). Experimental results demonstrate that EPARF can significantly improve both subjective and objective quality.
9260921
Complexity adaptive quantization for intra-frames in very low bit rate video coding
Conventional rate control schemes focus on the problem of finding an optimal quantization value for P- and B-frames. No rate control is available for the encoding of I-frames. This could pose big problems due to the large number of bits an I-frame could generate, and due to the fact that the number of bits varies drastically from sequence to sequence, depending on their image complexity. This problem becomes severe especially at very low bit rate. Therefore, a mechanism to allocate data bits to an I-frame according to its complexity is indispensable in order to have constant coding quality. This work presents a mechanism to establish for I-frames a generic relationship between the quantization value, data bits, and their image complexity. Experimental results show that this generic relationship provides a fairly accurate estimation of quantization value for an I-frame at given data bits and image complexity, and is very useful in controlling the data bits generated by an I-frame.
8588212
Temporal Aggregates in OWL-Time
In this paper, we describe our approach for representing temporal aggregates in OWL-Time, and a systematic way of mapping iCalendar recurrence sets to OWL-Time temporal sequences. We present several examples of natural language expressions for temporal aggregates and how they would be represented in OWL-Time; some examples can be described in both iCalendar and OWL-Time, while others can only be represented in OWL-Time. We also illustrate how we can translate from a natural language sentence to our representation.
11110859
A locally-adaptive algorithm for measuring blocking artifacts in images and videos
Block transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring blocking artifacts in images and videos. It exhibits unique and useful features: 1) it examines the blocks individually so that it can measure the severity of blocking artifacts locally; 2) it is a one-pass algorithm in the sense that the image needs to be accessed only once; 3) it takes into account the blocking artifacts for high bit rate images and the flatness for the very low bit rate images; 4) the blocking artifacts measure is well-defined in the range of 0-10. Experiments on various still images and videos show that this blockiness measure is very efficient in terms of computational complexity and memory usage, and can produce consistent blocking artifacts measurement.
2696556
Dynamic Modeling of Biotechnical Process Based on Online Support Vector Machine
Due to the complexity and high non-linearity of biotechnical process, most simple mathematical models cannot describe the behavior of biochemistry systems very well. Therefore, dynamic modeling of biotechnical process is indispensable. Support vector machine (SVM) is a novel machine learning method, which is powerful for the problem characterized by small sample, non-linearity, high dimension and local minima, and has high generalization. But currently most support vector machine regression (SVR) training algorithms are offline, which could not be suit for time-variant system. So an improved SVM called online support vector machine was presented to modeling for the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments. Some models based on SVM were also presented. The result shows that the modeling based online SVM is superior to modeling based on SVW.
16312564
A new hybrid model for time-series prediction
This paper proposed a new hybrid model in order to increase time series prediction accuracy. This hybrid model considers the routine time prediction technique like AR, ANN or any others as atomic building block. A linear hybrid technique is used to combine their forecast result into the final result. The hybrid algorithm was tested against three different kinds of time series data. Experiments results showed the effectiveness of the proposed hybrid model.
7252877
FARMER: finding interesting rule groups in microarray datasets
Microarray datasets typically contain large number of columns but small number of rows. Association rules have been proved to be useful in analyzing such datasets. However, most existing association rule mining algorithms are unable to efficiently handle datasets with large number of columns. Moreover, the number of association rules generated from such datasets is enormous due to the large number of possible column combinations.In this paper, we describe a new algorithm called FARMER that is specially designed to discover association rules from microarray datasets. Instead of finding individual association rules, FARMER finds interesting rule groups which are essentially a set of rules that are generated from the same set of rows. Unlike conventional rule mining algorithms, FARMER searches for interesting rules in the row enumeration space and exploits all user-specified constraints including minimum support, confidence and chi-square to support efficient pruning. Several experiments on real bioinformatics datasets show that FARMER is orders of magnitude faster than previous association rule mining algorithms.
12021051
COE: A General Approach for Efficient Genome-Wide Two-Locus Epistasis Test in Disease Association Study
The availability of high density single nucleotide polymorphisms (SNPs) data has made genome-wide association study computationally challenging. Two-locus epistasis (gene-gene interaction) detection has attracted great research interest as a promising method for genetic analysis of complex diseases. In this paper, we propose a general approach, COE, for efficient large scale gene-gene interaction analysis, which supports a wide range of tests. In particular, we show that many commonly used statistics are convex functions. From the observed values of the events in two-locus association test, we can develop an upper bound of the test value. Such an upper bound only depends on single-locus test and the genotype of the SNP-pair. We thus group and index SNP-pairs by their genotypes. This indexing structure can benefit the computation of all convex statistics. Utilizing the upper bound and the indexing structure, we can prune most of the SNP-pairs without compromising the optimality of the result. Our approach is especially efficient for large permutation test. Extensive experiments demonstrate that our approach provides orders of magnitude performance improvement over the brute force approach.
24069960
Study of Improved Hierarchy Genetic Algorithm Based on Adaptive Niches
Canonical genetic algorithms have the defects of pre-maturity and stagnation when applied in optimizing problems. In order to avoid the shortcomings, an adaptive niche hierarchy genetic algorithm (ANHGA) is proposed. The algorithm is based on the adaptive mutation operator and crossover operator to adjust the crossover rate and probability of mutation of each individual, whose mutation values are decided using individual gradient. This approach is applied in Percy and Shubert function optimization. Comparisons of niche genetic algorithm (NGA), hierarchy genetic algorithm (HGA) and ANHGA have been done by establishing a simulation model and the results of mathematics model and actual industrial model show that ANHGA is feasible and efficient in the design of multi-extremum.