• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.522-539
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• 2021

In order to solve the problems of the existing audio fingerprint method when extracting audio fingerprints from long speech segments, such as too large fingerprint dimension, poor robustness, and low retrieval accuracy and efficiency, a robust audio fingerprint retrieval method based on feature dimension reduction and feature combination is proposed. Firstly, the Mel-frequency cepstral coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) of the original speech are extracted respectively, and the MFCC feature matrix and LPCC feature matrix are combined. Secondly, the feature dimension reduction method based on information entropy is used for column dimension reduction, and the feature matrix after dimension reduction is used for row dimension reduction based on energy feature dimension reduction method. Finally, the audio fingerprint is constructed by using the feature combination matrix after dimension reduction. When speech's user retrieval, the normalized Hamming distance algorithm is used for matching retrieval. Experiment results show that the proposed method has smaller audio fingerprint dimension and better robustness for long speech segments, and has higher retrieval efficiency while maintaining a higher recall rate and precision rate.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.25-35
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• 2013

This paper focuses on building a generalized mood classification model with many mood classes instead of a personalized one with few mood classes. Two methods are adopted to improve the performance of mood classification. The one of them is feature reduction based on standard deviation of feature values, which is designed to solve the problem of lowered performance when all 391 features provided by MIR toolbox used to extract features of music. The experiments show that the feature reduction methods suggested in this paper have better performance than that of the conventional dimension reduction methods, R-Square and PCA. As performance improvement by feature reduction only is subject to limit, modular neural network is used as another method to improve the performance. The experiments show that the method also improves performance effectively.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.435-448
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• 2008

3D posture recognition is a solution to overcome the limitation of 2D posture recognition. There are many researches carried out for 3D posture recognition using 3D data. The 3D data consist of massive surface points which are rich of information. However, it is difficult to extract the important features for posture recognition purpose. Meanwhile, it also consumes lots of processing time. In this paper, we introduced a dimension reduction method that transform 3D surface points of an object to 2D data representation in order to overcome the issues of feature extraction and time complexity of 3D posture recognition. For a better feature extraction and matching process, a cylindrical boundary is introduced in meshless parameterization, its offer a fast processing speed of dimension reduction process and the output result is applicable for recognition purpose. The proposed approach is applied to hand and human posture recognition in order to verify the efficiency of the feature extraction.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.3
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• pp.116-121
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• 2006

In this paper, we propose the dimension reduction method of multi-dimension speech feature vector for real-time adaptation procedure in various noisy environments. This method which reduces dimensions non-linearly to map the likelihood of speech feature vector and noise feature vector. The LRT(Likelihood Ratio Test) is used for classifying speech and non-speech. The results of implementation are similar to multi-dimensional speech feature vector. The results of speech recognition implementation of detected speech data are also similar to multi-dimensional(10-order dimensional MFCC(Mel-Frequency Cepstral Coefficient)) speech feature vector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.7-11
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• 2010

In this paper, we propose an improved speech/music discrimination method based on a feature combination and dimensionality reduction approach. To improve discrimination ability, we use a feature based on spectral duration analysis and employ the hierarchical dimensionality reduction (HDR) method to reduce the effect of correlated features. Through various kinds of experiments on speech and music, it is shown that the proposed method showed high discrimination results when compared with conventional methods.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.74-88
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• 1995

This article introduces a unified method of choosing the most explanatory and significant multiway partitions for classification tree design and analysis. The method is derived on the impurity reduction (IR) measure of divergence, which is proposed to extend the proportional-reduction-in-error (PRE) measure in the decision-theory context. For the method derivation, the IR measure is analyzed to characterize its statistical properties which are used to consistently handle the subjects of feature formation, feature selection, and feature deletion required in the associated classification tree construction. A numerical example is considered to illustrate the proposed approach.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

• The Journal of the Acoustical Society of Korea
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• v.36 no.2
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• pp.144-150
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• 2017

Musical genre is indispensible in constructing music information retrieval system, such as music search and classification. In general, the spectral characteristics of a music signal are obtained based on a subband decomposition to represent the relative distribution of the harmonic and the non-harmonic components. In this paper, we investigate the subband decomposition parameters in extracting features, which improves musical genre classification accuracy. In addition, the linear projection methods are studied to reduce the resulting feature dimension. Experiments on the widely used music datasets confirmed that the subband decomposition finer than the widely-adopted octave scale is conducive in improving genre-classification accuracy and showed that the feature-dimension reduction is effective reducing a classifier's computational complexity.