26330758
Music Genre Classification Based on Multiple Classifier Fusion
Although researchers have made great progresses on music genre classification in recent years, the need for more accurate system is still not satisfied. In this paper, we propose a method for further reducing the classification error rate based on multiple classifier fusion. First of all, MFCCs and four features from MPEG-7 audio descriptor are extracted in every short time frame, and then a group of frames are gathered into a longer segment, in which mean and variance of these short time frames features are calculated. The segment is considered as the basic unit for training and testing module. Then random forest (RF) and multilayer perceptron neural network (MLP) are executed on such segment independently. Finally, a weighted voting fusion strategy is employed to fusion the result of the two classifiers on each segment, and the whole file decision is made by selecting the most frequently labeled genre over all the segments. Experiments showed that the approach is effective. The fusion result gets 12.4% relative reduction in error rate compared to our baseline system.
633050
Query by humming via multiscale transportation distance in random query occurrence context
Query by humming (QBH) is an interactive tool for retrieving favored songs from a large database of known media via acoustic input. In this task, common method for measuring similarity between query and candidate is either by symbolic notation distance or by framed based dynamic programming. However, the former has disadvantage of error-prone to the noted symbolic feature extraction stage, while the latter is time-consuming. It has been proved that transportation distance has its remarkable merit in image query. However, we adopt a new structure for handling QBH, which is based on an improved version of this measure in combination with a string searching algorithm. More practically, we extend this method in random piece context, which means users can hum at any part of music piece. Experimental results are evaluated in MIREX 2007. Final 92.82% MRR has shown its significant advantages.
12607701
An AIS-Based Service Match Model
Aiming at the problem that self-learning ability of the current system usually appears weak during service discovery, a novel artificial immune system (AIS) based service match model is proposed, which is based on the reference to the principles of cellpsilas mutation, evaluation and the quadratic response abilities, as well as simulation on the antibody-antigen identification mechanism. Theoretical analysis and simulations both show that the model can not only increasing the recall ratio and matching speed, but also realizing the function that deriving similar service request from known service request and then searching the best matching service. Furthermore, this service matching model is able to improve systempsilas adaptive learning, memorizing and dynamic evaluating abilities in service matching process.
46376386
Improving Image Annotations Using WordNet
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords.
26794680
Texture segmentation based on MRMRF modeling
Texture segmentation remains a fundamental issue in low-level image analysis, pattern recognition and computer vision. Texture segmentation problem can be solved in two directions: model fitting and non-parametric classification. In this paper, we propose to use multiresolution MRF (MRMRF) modeling in texture segmentation. A novel MRMRF parameter estimation method based on MCMC approach is presented. The experimental result shows that the method is suitable to segment textured images.
18228879
Research on Multi-objective Parameter Optimization Based on the Experimental Design and ANN-GA in the Digital Environment
Aiming at the multi-objective parameter optimization problem of black box system in the digital design/simulation environment, a multi-objective parameter optimization model based on the orthogonal design/uniform design and Artificial Neural Network- Genetic Algorithms ANN-GA is established. In this method, the principle of experimental design is used to arrange a test or virtual test project. The data modifying and data collecting in the virtual test are accomplished applying the characteristic of parameterization in the environment of digital simulation. At last, the neural network and Pareto genetic algorithm are adopted to optimize multi-objective parameters. A Pareto-optimal set of digital model can be found in specified region.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance