Multilayer complex network descriptors for color–texture characterization

Abstract

A new method based on complex networks is proposed for color-texture analysis. The proposal consists on modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT. Results among various literature methods are compared, including deep convolutional neural networks with pre-trained architectures. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers. and its results, comparisons with literature methods and discussions; finally, on Section 5 we discuss the main findings and results of the work. Theoretical Concepts and ReviewOn this section, we present the theoretical concepts of color-texture and CN. Color-Texture AnalysisThe goal of texture analysis is to study how to measure texture aspects and employ it to image characterization. Although this approach has been explored for many years, most works approach gray-level images, i.e. a single color-channel representing the pixel luminance. On this case, the multispectral information is either discarded or is not present. However, nowadays images that derive from different sources are mostly colored, for instance from the internet, surveillance cameras, satellites, microscopes, personal cameras and much more. The recent increase in computer hardware performance also makes possible to analyze larger amounts of data, allowing to keep the color information for texture analysis.In general, color-texture methods found in the literature are mostly integrative, which separate color from texture. These methods usually compute traditional gray-level descriptors from each color-channel, separately. For that, various gray-level methods can be applied or combined into an integrative descriptor. There are many graylevel methods found in literature. Classical techniques can be divided into statistical, model-based and structural methods [15]. The statistical methods were one of the first approaches which considered texture as a property to characterize images. Among statistical methods, the most common ones are those based on gray-level co-occurrence matrices [16,17] and Local Binary Patterns (LBP) [18]. Model-based methods include descriptors such as Gabor filters [19], which explore texture in the frequency domain, and Markov random field models [20]. Structural methods consists on analyzing the texture as a combination of various smaller elements, that are spatially arranged to compose the overall texture pattern. This is achieved, for...

Spatio-spectral networks for color-texture analysis

“…Monochromatic and opponent channels are obtained, calculated from the output of Gabor filters. More recently, some methods based on image complexity focused on the analysis of within-between channel aspects for color texture with fractal geometry [8] and CNs [30,44]. the vertex degree, which is the sum of its connections.…”

“…The concepts of CN applied to texture analysis have been explored and improved in more recent works. In [44] a new multilayer model is introduced for color texture analysis, where each network layer represents an image color channel, and its topology contains within-between channel connections in a spatial fashion. This work also proposes a new method for estimating optimal thresholding, and the use of the vertex clustering coefficient is also introduced for the network characterization, achieving promising results.…”

“…Therefore, as in [44], this neighborhood covers vertices in all color-channels because it considers only the spatial position of the pixels to compute its distance. This process allows to access the dynamic evolution as r increases by analyzing each network N r1 , ..., N rn , and have demonstrated to be effective for color texture characterization [44]. On the other hand, it is possible to notice that {G r1 vi , ..., G…”

“…This is a modification over the equations proposed in [44,45] so that neither side of the multiplication cancels the other when the intensity difference or the distance is 0 (same pixel in different channels), and to generate uniform values between [0, 1]. It is important to notice that according to the proposed neighboring, the creation of connections from all to all channels with the given connection weight implies that the network somehow performs opponent color processing in a spatial fashion.…”

“…This happens because a network that connects all pixels inside a fixed radius have constant topological measures such as degree, strength, clustering, etc. However, the thresholding leads to additional parameters that need to be tuned, which have been a drawback of previous works where authors explored costly techniques for optimal threshold selection [44,45]. In this sense, we propose a directed technique that eliminates the need for thresholding, reducing the method parameters to only the set of radius R = {r 1 , ..., r n }.…”