Automatic orientation detection of abstract painting

Abstract

Detecting the correct orientation of an image is an important part of computer vision and the image processing pipeline. To determine the orientation of abstract paintings, as a special image type with ambiguous content, is difficult. There are several problems in the current orientation detection research: one is the use of a great deal of low-level image features for classification; the second is that the input image size is typically required to be consistent when using a deep learning model. To solve these problems, we propose a multi-scale and multi-layer feature fusion Net (MMFF-Net). First, local binary patterns (LBP) were used to generate three LBP-RGB feature maps with different scales in RGB mode, which could express the rotation characteristics of the image well; secondly, a neural network model based on AlexNet and spatial pyramid pooling (SPP) was constructed. Two data sets were selected to compare the training effect of different model parameters and model structures. Compared with the experimental results of several models, the proposed model effectively improved the accuracy of the correct orientation detection of abstract painting images.