Batik image retrieval using convolutional neural network

This paper presents a simple technique for performing Batik image retrieval using the Convolutional Neural Network (CNN) approach. Two CNN models, i.e. supervised and unsupervised learning approach, are considered to perform end-to-end feature extraction in order to describe the content of Batik image. The distance metrics measure the similarity between the query and target images in database based on the feature generated from CNN architecture. As reported in the experimental section, the proposed supervised CNN model achieves better performance compared to unsupervised CNN in the Batik image retrieval system. In addition, image feature composed from the proposed CNN model yields better performance compared to that of the handcrafted feature descriptor. Yet, it demonstrates the superiority performance of deep learning-based approach in the Batik image retrieval system

Extraction of object image features with gradation contour

Image retrieval using features has been used in previous studies including shape, color, texture, but these features are lagging. With the selection of high-level features with contours, this research is done with the hypothesis that images on objects can also be subjected to representations that are commonly used in natural images. Considering the above matters, we need to research the feature extraction of object images using gradation contour. From the results of the gradation contour test results, there is linearity between the results of accuracy with the large number of images tested. Therefore, it can be said that the influence of the number of images will affect the accuracy of classification. The use of contour gradation can be accepted and treated equally in all image types, so there is no more differentiation between image features. The complexity of the image does not affect the method of extracting features that are only used uniquely by an image. From the results of testing the polynomial coefficient savings data as a result of the gradation contour, the highest result is 81.40% with the highest number of categories and the number of images tested in the category is also higher