Performance Comparison of Saliency Detection

Received Dec 27, 2017; Revised Mar 29, 2018; Accepted Apr 29, 2018

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1. Introduction

Human vision has the incredible capacity to detect visually distinct objects and regions of interest in these images. Research on human vision can effectively solve application problems in computer vision. And scientists have become interested in its capability to find objects or regions representing a scene, which is generally referred to as saliency detection. Figure 1 depicts the concept of saliency detection. The original images are shown in the first row. In saliency detection, significant objects or regions are determined and distinguished from the background, as shown in the second row in Figure 1.

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Generally, the process includes two steps: $(1)$ object location, accurate location is premise, and $(2)$ segment object or region. So far, there have been kinds of segmentation algorithms proposed [1]. The proposed models have solved the first problem well on scenes with the single objects. However, how to accurately detect multiple objects on scenes with high-clutter background is still a problem.

1.1. Definitions

Saliency detection is generally described as the automatic estimation of significant (salient) objects and regions of images without any prior assumption or knowledge. Saliency usually is described as difference between a pixel and its surrounding neighborhood [2]. In addition, saliency models (i.e., models of saliency detection) include visual models, purely computational models, and their combination. Purely computational model does not consider the visual characteristics of the human eye whereas others do. Generally, more attention is on visual models, which can be roughly classified into two categories: attention models (predicting fixation points) and salient region detection models (highlighting the whole salient object areas). The former uses the selective visual attention mechanism to dynamically sample the important visual content in the scene. These models acquire a series of visual fixation points which locate the salient objects. The latter detects and segments the whole object or region. In this paper, we divide the latter into two more detailed categories: salient region detection and salient object detection.

1.2. Origin...

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