Development of a perception oriented texture-based image retrieval system for wallpapers.

Due to advances in computer technology, large image collections have been digitised and archived in computers. Image management systems are therefore developed to retrieve relevant images. Because of the limitations of text-based image retrieval systems, Content-Based Image Retrieval (CBIR) systems have been developed. A CBIR system usually extracts global or local contents of colour, shape and texture from an image to form a feature vector that is used to index the image. Plethora methods have been developed to extract these features, however, there is very little in the literature to study the closeness of each method to human perception. This research aims to develop a human perception oriented content-based image retrieval system for the Museum of Domestic Design & Architecture (MoDA) wallpaper images. Since texture has been widely regarded as the main feature for these images and applied in CBIR systems, psychophysical experiments were conducted to study the way human perceive texture and to evaluate five popular computational models for texture representations: Grey Level Co-occurrence Matrices (GLCM), Multi-Resolution Simultaneous Auto-Regressive (MRSAR) model, Fourier Transform (FT), Wavelet Transform (WT) and Gabor Transform (GT). By analyzing experimental results, it was found that people consider directionality and regularity to be more important in terms of texture than coarseness. Unexpectedly, none of the five models appeared to represent human perception of texture very well. It was therefore concluded that classification is needed before retrieval in order to improve retrieval performance and a new classification algorithm based on directionality and regularity for wallpaper images was developed. The experimental result showed that the evaluation algorithm worked effectively and the evaluation experiments confirmed the necessity of the classification step in the development of CBIR system for MoDA collections

Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints

This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only