Texture Similarity Evaluation via Siamese Convolutional Neural Network
Lukáš Hudec, Wanda Benešová · 2018
Image texture analysis, texture description, and texture similarity evaluation are important areas in computer vision. Evaluation of similarity is needed in many areas as for example object segmentation or image retrieval. We introduce a novel approach for texture similarity measure based on modern deep learning techniques. Our goal is to determine the similarity between patches from homogeneous and also non-homogeneous textures of real-world images. We took the advantage of Siamese Neural Network which is designed to determine the similarity of image pairs. Siamese Neural Network learns to select the most distinctive features responsible for differentiation of the textures. Each of the twin networks creates a feature vector for the image it processes. We used Euclidean and Canberra distance as the similarity metrics to compare these vectors. The final results of the evaluation show a great potential of the proposed method.