Active Object Detection Model with Deep Neural Network for Object Recognition - Kapila, R; Wadhwa, H (International Journal of Computer Sciences and Engineering, 2018)
The number of the computations and feature transformations along with the normalization and automatic categorization is required by the object classification algorithms. In this paper, the robust feature descriptor used with the active object detection method (AODM) along with the probabilistic equation enabled deep neural networks (DNN). The multi-category DNN (mDNN) has been described with the repetitious phases so that it is simple to do job with the multi-category dataset. In every iterative phase mDNN shows the training data of main class as primary class and remaining all other training data are divided as the secondary class for the supervised classification. In the object image dataset, designed model is proficient of working with the variations which are observed in the configuration of the color, texture, light, image orientation, and occlusion and color illuminations. Certain analysis has been organized over the designed model for the performance calculation of the object identification system in the designed model. The results which we collected are in the shape of the various performance parameters of statistical errors, precision, recall, F1-measure and overall accuracy. In the terms of the overall accuracy the designed model has clearly outperformed the existing models. The designed model growth has been recorded higher than ten percent for all of the evaluated parameters against the existing models based upon SURF, FREAK, etc.