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International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Herdiyeni, Yeni Santoni, Mayanda Mega Bogor Agr Univ Fac Mathemat & Nat Sci Dept Comp Sci Bogor Indonesia

ISBN: (纸本)9781467330268

We propose a new method for Indonesian medicinal plants identification using combination of some leaf features, i.e. texture, shape, and color. local binary pattern variance (LBPV) is used to extract leaf texture, morphological feature is used to extract leaf shape, and color moment is used to extract leaf color distribution. In the experiment we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. Combination of leaf feature is done using Product Decision Rule (PDR) and classification of medicinal plants is done using Probabilistic Neural Network (PNN). The experimental results show that the combination of the morphological, LBPV, and color moments features can improve the accuracy of medicinal plants identification. This research is important to enhance utilization of Indonesian medicinal plants.

关键词： color moments local binary pattern variance Indonesian medicinal plant identification morphological probabilistic neural network

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local binary pattern and its derivatives for face recognition

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IET COMPUTER VISION 2012年第5期6卷 480-488页

作者： Suruliandi, A. Meena, K. Rose, R. Reena Manonmaniam Sundaranar Univ Dept Comp Sci & Engn Tirunelveli Tamil Nadu India Sardar Raja Coll Engn Dept Comp Sci & Engn Tirunelveli 627808 Tamil Nadu India St Xaviers Catholic Engn Coll Dept Comp Sci & Engn Kanyakumari Tamil Nadu India

Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The local binary pattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric local binary pattern, local binary pattern variance, dominant local binary pattern, advanced local binary pattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.

关键词： multivariate local binary pattern YALE databases advanced local binary pattern face recognition systems LBP-based models angle variations Image recognition LDP Spatial and pictorial databases visual databases facial expression variations centre symmetric local binary pattern local derivative pattern surveillance face recognition local binary pattern texture method surface property illumination changes Computer vision and image processing techniques Japanese female facial expression FRGC version2 databases image texture local binary pattern variance identity verification dominant local binary pattern

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Video Clip Retrieval: An Integrated Approach based on KDM and LBPV

Video Clip Retrieval: An Integrated Approach based on KDM an...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Uma, K. P. Shekar, B. H. Smitha, M. L. Mangalore Univ Dept Comp Sci Mangalore Karnataka India KVG Coll Engn Dept Comp Sci Sullia Karnataka India

ISBN: (纸本)9781509063673

Videos are warehouse of information. A good retrieval system is essential for timely access to definitive and accurate information. This paper presents an approach for retrieving shots containing similar text using query by example paradigm. localization of text is based on Kirsch Direction Mask (KDM). From the localized text region, texture features viz. local binary pattern variance (LBPV) are obtained. Using LBPV features shots/clips with similar text in a video are retrieved. Experiments conducted on a collection of videos demonstrates that the proposed approach is suitable for video retrieval task.

关键词： local binary pattern variance Kirsch Directional Mask Key Frame Text based Video Retrieval

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Multi Layer Kernel Learning for Time Series Forecasting

Multi Layer Kernel Learning for Time Series Forecasting

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International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Widodo, Agus Budi, Indra Univ Indonesia Fac Comp Sci Informat Retrieval Lab Depok Indonesia

ISBN: (纸本)9781467330268;9789791421157

Multiple Kernel Learning (MKL) is one of recent approaches to choose suitable kernels from a given pool of kernels by exploring the combinations of multiple kernels. For linear kernel, the target kernel is a linear combination some base kernels. However, some literatures suggest that a linear combination of kernels cannot consistently outperform either the uniform combination of base kernels or simply the best single kernel. Hence, some researchers attempt to study the non-linear combination of kernels, such as polynomial combination of kernels or two-layer MKL. This paper extends the previous work on two-layer MKL into three-layer MKL especially in the field of regression to forecast future values of time series. Our experiment on several time series dataset demonstrates that our proposed method generally outperforms the linear combination of kernels.

关键词： Indonesian medicinal plant identification color moments local binary pattern variance morphological probabilistic neural network Seeds Kernel Linear Combination probabilistic neural network Time series time series datasets multilayers

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Animal Sound Recognition Based on Double Feature of Spectrogram in Real Environment

Animal Sound Recognition Based on Double Feature of Spectrog...

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7th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Li, Ying Wu, Zhibin Fuzhou Univ Coll Math & Comp Sci Fuzhou 350002 Peoples R China

ISBN: (纸本)9781467376877

In this paper, we propose an animal sound recognition method in various noise environments with different Signal-to-Noise Ratios (SNRs). In real world, the ability to automatically recognize a wide range of animal sounds can analyze the habits and distributions of animals, which makes it possible to effectively monitor and protect them. However, due to the existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low SNR condition. To address this problem, this paper proposes double feature, which consists of projection feature and local binary pattern variance (LBPV) feature, combined with random forests for animal sound recognition. In feature extraction, an operation of projecting is made on spectrogram to generate the projection feature. Meanwhile, LPBV feature is generated by means of accumulating the corresponding variances of all pixels for every uniform local binary pattern (ULBP) in the spectrogram. As the experimental results show, the proposed method can recognize a wide range of animal sounds and still remains a recognition rate over 80% even under 10dB SNR.

关键词： Animal sound recognition local binary pattern variance projection feature random forests

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Video Clip Retrieval Based on LBP variance 12

Video Clip Retrieval Based on LBP Variance

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12th International Conference on Communication Networks (ICCN) / 12th International Conference on Data Mining and Warehousing (ICDMW) / 12th International Conference on Image and Signal Processing (ICISP)

作者： Shekar, B. H. Uma, K. P. Holla, K. Raghurama Mangalore Univ Mangalore Karnataka India

Advancement in technology has made the acquisition and storage of multimedia data easy and inexpensive to the end user. However for effective use of the information available in the multimedia, efficient and accurate retrieval methods are required. Multimedia based retrieval systems extensively used texture based approach to interpret and recognize a scene image. The texture of an image provides clue to the orientation, smoothness, symmetry, shape, regularity and coarness of the surface. The local binary pattern variance (LBPV) is a texture feature where variance in contrast acts as adaptive factor during computation of local binary pattern (LBP) histogram. The LBP combines both structural and statistical approaches to texture analysis. This paper proposes an LBP variance based approach to visual content based video retrieval. The proposed approach uses query by example paradigm for retrieving similar clips from the video. Experiments conducted on TRECVID dataset shows the efficacy of proposed approach. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Content Based Retrieval local binary pattern variance Multimedia Query by Example

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Video Clip Retrieval Based on LBP variance

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Procedia Computer Science 2016年 89卷 828-835页

作者： B.H. Shekar K.P. Uma K. Raghurama Holla Mangalore University Mangalore Karnataka India

关键词： Content Based Retrieval local binary pattern variance Multimedia Query by Example

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