Auto-Associative Features with Non-Iterative Learning Based Technique for Image Classification

Image classification is a fundamental task that attempts to apprehend an entire image by assigning a specific class or label. Accurate feature extraction techniques are the main building blocks of any image classification and prediction application. Most of these feature extraction techniques are manual (hand-crafted), hence they contain tedious, challenging, and erroneous tasks. In this paper, a new automatic technique for image classification is presented in which a novel concept to extract auto-associative features along with a non-iterative learning is investigated. The proposed technique incorporates an auto-associative neural network for most accurate feature extraction and a noniterative classifier. The proposed technique has been evaluated on benchmark datasets. The experimental results have demonstrated that the proposed technique can achieve same or slightly higher accuracy than the standard and formula-based techniques.

图像分类是一项旨在通过分配特定的类别或标签来全面把握整幅图像的基本任务。精确的特征提取技术是任何图像分类与预测应用的主要构建模块。大多数这些特征提取技术均为手工制作（手工构建），因此它们包含了繁琐、具有挑战性和可能存在错误的任务。在本文中，提出了一种新的图像分类自动技术，该技术研究了提取自关联特征的非迭代学习的新概念。所提出的技术集成了自关联神经网络以实现最精确的特征提取和非迭代分类器。该技术已在基准数据集上进行了评估。实验结果表明，所提出的技术能够达到与标准及基于公式的技术相同或略高的准确性。