Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint

Abstract

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture separately and therefore implicitly consider them as independent features which is not the case in reality. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. It is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by a vector whose components are from a superpixel obtained by the SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm, which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-textures is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling) that considers the color micro-textures’ non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB (Red–Green–Blue), HSL (Hue–Saturation–Luminance), LUV (L for luminance, U and V represent chromaticity values) and CMY (Cyan–Magenta–Yellow) color space. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error), MSE (Mean Squared Error) and Fβ measures of our saliency maps, on the five most used datasets show that our model outperformed several state-of-the-art models. Being simple and efficient, our model could be combined with classic models using color contrast for a better performance.

CoSOV1Net: A Cone- and Spatial-Opponent Primary Visual Cortex-Inspired Neural Network for Lightweight Salient Object Detection

“…First, at the beginning of the neural network, our model opposes color channels two by two by grouping them (R-R, R-G, R-B, G-G, G-B, B-B) then extracting the features at the channels’ spatial levels and between the color channels from each channel pair at the same time, to integrate color into patterns. Therefore, instead of performing a subtractive comparison or an OCLTP (opponent color linear ternary pattern) like Ndayikengurukiye and Mignotte [ 1 ], we let the neural network learn the features that represent the comparison of the two color pairs. Second, this idea of grouping and then extracting the features at the channels’ spatial levels and between the color channels at the same time is applied on feature maps at each neural network level until the saliency maps are obtained.…”

“…At this stage, through Pairing_Color_Unit, the input RGB image is paired in six opposing color channel pairs: R-R, R-G, R-B, G-G, G-B and B-B [ 1 , 35 , 48 ]. These pairs are then concatenated, which gives 12 channels, R, R, R, G, R, B, G, G, G, B, B, B, as illustrated in Figure 3 .…”

“…To extract features for salient object detection, Ndayikengurukiye and Mignotte [ 1 ] used nine (9) opponent channels for RGB color space (RR: red–red; RG: red–green; RB: red–blue; GR: green–red; GG: green–green; GB: green–blue; BR: blue–red; BG: blue–green; BB: blue–blue) with a nonlinear combination, thanks to the OCLTP (opponent color local ternary pattern) texture descriptor, which is an extension of the OCLBP (opponent color local binary pattern) [ 35 , 36 ] and Fastmap [ 37 ], which is a fast version of MDS (multidimensional scaling).…”

“…The human visual system (HVS) has the ability to select and process relevant information from among the large amount that is received. This relevant information in an image is called salient objects [ 1 ]. Salient object-detection models in computer vision try to mimic this phenomenon by detecting and segmenting salient objects in images.…”

Brain Tumor Segmentation and Classification Using Binomial Thresholding-Based Bidirectional-Long-Short Term Memory

“…LTP is [25] employed in the image processing as feature extraction. It is a Local Binary Pattern (LBP) extension in which the 𝑠(𝑧) function is expressed in Eq.…”