Browsing by Author "Parinya Sanguansat"

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    An improved feature extraction and combination of multiple classifiers for query-by-humming
    (Zarka Private Univ, 2014) Nattha Phiwma; Parinya Sanguansat
    In this paper, we propose new methods for feature extraction and soft majority voting to adjust efficiency and accuracy of music retrieval. For our work, the input is humming sound which is sound wave and Musical Instrument Digital Interface (MIDI) is used as the reference song in database. A critical issue of humming sound are variation such as duration, sound, tempo, key, and noise interference from both environment and acquisition instruments. Besides all the problems of humming sound we have mentioned earlier, whether humming sound and MIDI in different domain which will make the difficulty for two domains to compare each other. However, to make these two in the same domain, we convert them into the frequency domain. Our approach starts from pre-processing by using features for note segmentation by humming sound. The process consists of four steps as follows: Firstly, the MIDI is already a sequence of pitch while the pitch in humming sound is needed to extract by Subharmonic-to-Harmonic (SHR). Subsequently, the extracted pitch can be used to calculate all above attributes and then multiple classifiers are applied to classify the multiple subsets of these features. Afterwards, the subset contain the multiple attributes, Multi-Dimensional Dynamic Time Warping (MD-DTW) is used for similarity measurement. Finally, Nearest Neighbours (NN) and soft majority voting are used to obtain the retrieval results in case of equal scores. From the experiments, to achieve 100% accuracy rate at the early top-n rank in retrieving, the appropriate feature set should consist of five classifiers.
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    Classification via k-means clustering and distance-based outlier detection
    (2012) Surasit Songma; Witcha Chimphlee; Kiattisak Maichalernnukul; Parinya Sanguansat
    We propose a two-phase classification method. Specifically, in the first phase, a set of patterns (data) are clustered by the k-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The Knowledge Discovery Databases (KDD) Cup 1999 data set, which has been utilized extensively for development of intrusion detection systems, is used in our experiment. The results show that the proposed method is effective in intrusion detection. © 2012 IEEE.
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    TF-RNF: A novel term weighting scheme for sports video classification
    (2012) Prisana Mutchima; Parinya Sanguansat; P. Mutchima; Department of Information Technology, Suan Dusit Rajabhat University, Thailand; email: prisanut@hotmail.com
    Determination of content importance is very important in achieving high quality classification. Term weighting schemes in text classification will be applied to classify videos by measuring importance of video contents. In other words, a video sequence can be treated as a document, and frames of a video are considered as words or terms which identify contents of a video. And to enhance the efficiency of video classification, this paper proposes a novel term weighting scheme, called the Term Frequency - Relevance and Non-relevance Frequency (TF-RNF) weighting. This technique can filter both relevant and non-relevant contents so as to reduce classification errors. Empirical evaluations of results show that the proposed technique significantly outperforms traditional techniques in sports video classification. © 2012 IEEE.