Network and layer experiment using convolutional neural network for content based image retrieval work
10.12928/telkomnika.v20i1.19759
Fachruddin; Faculty of Engineering, Universitas Sriwijaya, Palembang Fachruddin
,
Saparudin; Informatics Department, Telkom University, Bandung Saparudin
,
Errissya; Universitas Dinamika Bangsa Rasywir
,
Yovi; Universitas Dinamika Bangsa Pratama
In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.
