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  • Open Access

    ARTICLE

    Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

    Vidit Kumar1,*, Hemant Petwal2, Ajay Krishan Gairola1, Pareshwar Prasad Barmola1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2711-2724, 2023, DOI:10.32604/csse.2023.032047

    Abstract Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded… More >

  • Open Access

    ARTICLE

    Machine Learning-based Detection and Classification of Walnut Fungi Diseases

    Muhammad Alyas Khan1, Mushtaq Ali1, Mohsin Shah2, Toqeer Mahmood3, Muneer Ahmad4, NZ Jhanjhi5, Mohammad Arif Sobhan Bhuiyan6,*, Emad Sami Jaha7

    Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 771-785, 2021, DOI:10.32604/iasc.2021.018039

    Abstract Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization.… More >

  • Open Access

    ARTICLE

    The Application of Sparse Reconstruction Algorithm for Improving Background Dictionary in Visual Saliency Detection

    Lei Feng1,2, Haibin Li1,*, Yakun Gao1, Yakun Zhang1

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 831-839, 2020, DOI:10.32604/iasc.2020.010117

    Abstract In the paper, we apply the sparse reconstruction algorithm of improved background dictionary to saliency detection. Firstly, after super-pixel segmentation, two bottom features are extracted: the color information of LAB and the texture features of the image by Gabor filter. Secondly, the convex hull theory is used to remove object region in boundary region, and K-means clustering algorithm is used to continue to simplify the background dictionary. Finally, the saliency map is obtained by calculating the reconstruction error. Compared with the mainstream algorithms, the accuracy and efficiency of this algorithm are better than those of other algorithms. More >

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