Substantiation of location image classification model using projective template matching and convolutional neural network

Abstract

This study first attempts to observe the action of the CNN and then compares it to test Projective Template Matching and Object Detection as new approaches. In the final model selection, the accuracy of the prediction model and the computational processing time was mainly compared. At last, the combination of the Object Detection model and CNN was selected as a final location classification model with a prediction accuracy of 61%. This final model shows the optimal prediction result by first attempting to detect the common feature regions of the location image and then analyzing the overall feature characteristic. The fact is that CNN is good for training image data with common overall features for classification. This being so, we expect that training several fundamental ROIs can more efficiently train the CNN model than training the pure location images.