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.