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  • Open Access


    Hybrid Trainable System for Writer Identification of Arabic Handwriting

    Saleem Ibraheem Saleem*, Adnan Mohsin Abdulazeez

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3353-3372, 2021, DOI:10.32604/cmc.2021.016342

    Abstract Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word’s attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient… More >

  • Open Access


    1D-CNN: Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

    Mustaqeem, Soonil Kwon*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4039-4059, 2021, DOI:10.32604/cmc.2021.015070

    Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated… More >

  • Open Access


    Human Action Recognition Based on Supervised Class-Specific Dictionary Learning with Deep Convolutional Neural Network Features

    Binjie Gu1, *, Weili Xiong1, Zhonghu Bai2

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 243-262, 2020, DOI:10.32604/cmc.2020.06898

    Abstract Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human More >

  • Open Access


    Instance Retrieval Using Region of Interest Based CNN Features

    Jingcheng Chen1, Zhili Zhou1,2,*, Zhaoqing Pan1, Ching-nung Yang3

    Journal of New Media, Vol.1, No.2, pp. 87-99, 2019, DOI:10.32604/jnm.2019.06582

    Abstract Recently, image representations derived by convolutional neural networks (CNN) have achieved promising performance for instance retrieval, and they outperform the traditional hand-crafted image features. However, most of existing CNN-based features are proposed to describe the entire images, and thus they are less robust to background clutter. This paper proposes a region of interest (RoI)-based deep convolutional representation for instance retrieval. It first detects the region of interests (RoIs) from an image, and then extracts a set of RoI-based CNN features from the fully-connected layer of CNN. The proposed RoI-based CNN feature describes the patterns of More >

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