Salient Region Detection and Segmentation in Images using Dynamic Mode Decomposition

TL;DR

This paper introduces a novel method for salient region detection in images using Dynamic Mode Decomposition, combining Fourier Transform and PCA to create color and luminance-based saliency maps, showing improved results over existing methods.

Contribution

The paper presents a new approach applying DMD to static image saliency detection, integrating color and luminance information for enhanced accuracy.

Findings

Effective saliency detection demonstrated through ROC, F-measure, and AUC scores.

Outperforms previous state-of-the-art saliency estimation methods.

Utilizes DMD with color and luminance cues for improved visual attention modeling.

Abstract

Visual Saliency is the capability of vision system to select distinctive parts of scene and reduce the amount of visual data that need to be processed. The presentpaper introduces (1) a novel approach to detect salient regions by considering color and luminance based saliency scores using Dynamic Mode Decomposition (DMD), (2) a new interpretation to use DMD approach in static image processing. This approach integrates two data analysis methods: (1) Fourier Transform, (2) Principle Component Analysis.The key idea of our work is to create a color based saliency map. This is based on the observation thatsalient part of an image usually have distinct colors compared to the remaining portion of the image. We have exploited the power of different color spaces to model the complex and nonlinear behavior of human visual system to generate a color based saliency map. To further improve the effect of final saliency map, weutilized luminance information exploiting the fact that human eye is more sensitive towards brightness than color.The experimental results shows that our method based on DMD theory is effective in comparison with previous state-of-art saliency estimation approaches. The approach presented in this paperis evaluated using ROC curve, F-measure rate, Precision-Recall rate, AUC score etc.