Department of Information Engineering (DEI), Via Gradenigo 6, 35131 Padova, Italy.
Department of Information Technology and Cybersecurity, Missouri State University, 901 S, National Street, Springfield, MO 65804, USA.
Sensors (Basel). 2021 Feb 24;21(5):1573. doi: 10.3390/s21051573.
Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system's performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.
传统上，分类器是通过在特征空间中预测模式来进行训练的。这里提出的图像分类系统通过组合由大量孪生神经网络 (SNN) 生成的差异空间来训练分类器，以在向量空间中预测模式。通过监督 k-均值聚类计算来自训练数据集的模式的一组质心。质心通过 Siamese 网络用于生成差异空间。通过将模式投影到相似性空间上来提取向量空间描述符，并且 SVM 通过其差异向量对图像进行分类。通过在两个领域的不同类型的图像上评估系统，证明了所提出的方法在图像分类中的多功能性：两个医学数据集和两个具有代表为图像（频谱图）的发声的动物音频数据集。结果表明，所提出的系统的性能与文献中表现最好的方法竞争激烈，在其中一个医学数据集上获得了最先进的性能，并且在没有对测试数据集上的聚类方法进行特定优化的情况下实现了这一点。
