A deep neural network and classical features based scheme for objects recognition: an application for machine inspection

• Published in: Multimedia tools and applications 2024-02, Vol.83 (5), p.14935-14957
• Main Authors: Hussain, Nazar, Khan, Muhammad Attique, Sharif, Muhammad, Khan, Sajid Ali, Albesher, Abdulaziz A., Saba, Tanzila, Armaghan, Ammar
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Automation; Classification; Computer Communication Networks; Computer Science; Computer vision; Data augmentation; Data Structures and Information Theory; Deep learning; Inspection; Machine learning; Multimedia Information Systems; Object recognition; Special Purpose and Application-Based Systems; Supervised learning
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-08852-3

Computer Vision (CV) domain is widely used in the current era of automation and visual surveillance for the detection and classification of different objects in a diverse environment. The automatic machine inspection of different objects in the scenes is based on internal and external parameters like features that provide a huge amount of information related to the nature of an object in the scene. In this work, we propose a new automated method based on classical and deep learning feature selection. The proposed object classification method follows three steps. The data augmentation is performed in the first step to make the balance database. Later, Pyramid HOG (PHOG) and Central Symmetric LBP (CS-LBP) features are serially fused along with deep learning-based extracted features. The deep learning features are extracted from the pre-trained CNN model name Inception V3. In the third step, a new technique name Joint Entropy along with KNN (JEKNN) is employed to select the best features. The best-selected features are finally classified by well-known supervised learning methods and choose the best one based on higher accuracy. The proposed method is evaluated on Caltech101 balanced dataset and achieved maximum accuracy of 90.4% on Ensemble classifier which outperforms as compare to existing techniques.