# Active Contours in the Complex Domain for Salient Object Detection
## 1 Introduction  
Salience detection is a key problem for computer vision and image processing. It plays a vital role in many applications, facilitating object tracking [1; 2; 3], image and video compression [4], remote sensing image segmentation [5], video surveillance [6], and image retrieval [7]. However, salience detection faces many challenges, such as noisy and complex backgrounds [8] and intensity inhomogeneity [9; 10; 11]. These challenges make the current solutions very uncertain. Researchers are working to address these limitations and improve the accuracy of salient object detection. Salience detection [12; 13; 14] extracts important and meaningful regions from an image [15]. It finds the most informative and interesting regions. During the last few decades, salience detection has been widely investigated due to its similarity to the human visual system and it has been popularly applied in computer vision. It is closely related to selective processing in the human visual system. Humans can capture or select relevant information within a large visual field. The human visual system is sensitive to salient regions and can quickly identify saliency for additional cognitive processing. Salience detection mimics the human capability to automatically discover salient regions. There are two types of motivated approaches in salience detection [16] to combine object information: bottom up and top down. Bottom-up methods only use the visual signal. They automatically estimate meaningful objects without any prior assumptions or knowledge [16]. Top-down methods work from some visual priors [17; 18]. Our proposed model bis a bottom-up approach, which does not require any prior knowledge. In recent years, many models have been proposed for salience detection. Inspired by the biological model, [PERSON] and [PERSON] [19] proposed a salience model that shows the original concepts of salient objects, representing visual attention. Their model uses biological inspiration, which is, the center-surround strategy of color and intensity, for identifying the salient region in an image. [PERSON] et al. [20] presented a fuzzy growing method to squeeze out regions of interest and extracted saliency while using local contrast. The model for eye fixation that was proposed by [PERSON] et al. [21] introduced an attentive object maximization model from eye fixation. The model uses [PERSON]'s self-information for salient region detection. [PERSON] et al. [22] extracted a saliency map by taking advantage of the global region contrast of the image and defined a spatial relation on the region for salience detection. The bottom-up approach that was used by [PERSON] et al. [23] developed a saliency model that merges low-level features, mid-level features, and high-level features to extract the salient object. However, the local contrast cannot handle the global influence and, thus, only detects the boundaries of the object. [PERSON] et al. [24] proposed a model that uses a Bayesian formwork to exploit low-level and mid-level cues. However, these methods are facing the limitation of computation cost and unsatisfactory results in object detection. We have proposed novel, automatic, and accurate active contour model to address and fixed these limitations. Our unit for salience detection utilizes a global mechanism. The global mechanism depends on the global context to explore salient cues. Existing active contour models can be classified into two categories: region-based [25; 26; 27; 28; 29; 30; 31; 32; 33; 34] and edge-based [35; 36]. Region-based active contour models utilize the statistical information of image intensity in different subsets. Edge-based active contour models use edge indicators to localize the boundaries. [PERSON] and [PERSON] [29] proposed a region-based model that adds curve length as a penalty term. The method uses intensity statistics for object segmentation and fails when inhomogeneity is present. [PERSON] and [PERSON] [37] proposed a piecewise smooth-function approximation for separate regions.