# Curiosity-Driven Camouflaged Object Segmentation
## 4 Experiments  
### Experimental Setup  
**Datasets. ** We conduct experiments on three benchmark datasets for camouflaged object segmentation: CAMO [17], COD10K [15], and NC4K [16]. CAMO [17] comprises 1250 camouflaged and 1250 non-camouflaged images. COD10K [15] consists of 5066 camouflaged, 3000 background, and 1934 non-camouflaged images. NC4K [16] encompasses 4121 images sourced from the Internet. Conforming to the data partition strategy outlined in [15; 16; 21], we utilize all images containing camouflaged objects for our experimental evaluations. Specifically, our training set consists of 3040 images from COD10K and 1000 images from CAMO, while the remaining images are reserved for testing purposes. **Evaluation Metrics. ** To comprehensively compare our proposed method with other state-of-the-art methods, we employ five widely-used metrics to evaluate the COS performance. The details of each metric are provided as follows. (1) **PR Curve:** PR curve is drawn with precision and recall as variables. Recall is the transverse coordinate and precision is the vertical coordinate. Specifically, given a saliency map \(S\), we can convert it to a binary mask \(M\), and then compute the precision and recall by comparing \(M\) with ground-truth \(G\):  
\[Precision=\frac{M\cap G}{M},Recall=\frac{M\cap G}{G} \tag{7}\]  
Then, we adopt a popular strategy to partition the saliency map \(S\) using a set of thresholds (i.e., from 0 to 255). For each threshold, we first calculate a pair of recall and precision scores and then combine them to obtain a PR curve that describes the performance of the model at the different thresholds. (2) **Structure Measure (\(S_{n}\)) [34]:** It is proposed to assess the structural similarity between the regional perception (\(S_{r}\)) and object perception (\(S_{o}\)), which is defined by  
\[S_{n}=\alpha\times S_{o}+(1-\alpha)\times S_{r} \tag{8}\]  
where \(\alpha\in[0,1]\) is a trade-off parameter and it is set to 0.5 as default. (3) **Enhanced-alignment Measure (\(E_{\phi}\)) [35]:** It is used to capture image-level statistics and their local pixel matching information, which is defined by  
\[E_{\phi}=\frac{1}{W\times H}\sum_{i=1}^{W}\sum_{j=1}^{H}\phi(S(x,y),G(x,y)) \tag{9}\]  
where \(W\) and \(H\) denote the width and height of ground-truth \(G\), and \((x,y)\) is the coordinate of each pixel in \(G\). Symbol \(\phi\) is the enhanced alignment matrix. This metric includes three values computed over all the thresholds, i.e., maximum (\(E_{\phi}^{mx}\)), mean (\(E_{\phi}^{mn}\)), and adaptive (\(E_{\phi}^{ad}\)) values. In our experiments, we adopt the adaptive (\(E_{\phi}^{ad}\)) values. (4) **F-measure (\(F_{\beta}\)) [36]:** It is used to comprehensively consider both precision and recall, and we can obtain the weighted harmonic mean by  
\[F_{\beta}=\Big{(}1+\beta^{2}\Big{)}\frac{Precision\times Recall}{\beta^{2} Precision+Recall} \tag{10}\]  
where _Precision_ and _Recall_ are defined by Formula (7). Recent studies [34; 35] have suggested that the weighted F-measure (\(F_{\beta}^{av}\)) [36] can provide more reliable evaluation results than the traditional \(F_{\beta}\). Thus, we consider this metric in the comparison. (5) **Mean Absolute Error (\(M\)) [37]:** It is used in foreground-background-segmentation tasks, which calculates the element-wise difference between the prediction map and the ground truth mask. It is defined by  
\[M=\frac{1}{W\times H}\sum_{i=1}^{W}\sum_{j=1}^{H}\lvert S(i,j)-G(i,j)\rvert \tag{11}\]  
where \(G\) and \(S\) denote the ground truth and normalized prediction (it is normalized to \([0,1]\)). **Implementation Details. ** We implement our network using the publicly available Pytorch toolbox [38]. A ten-core PC with an Intel Core i9-10900X 3.70 GHz CPU (with 32 GB memory) (Intel, Santa Clara, CA, USA) and an NVIDIA GeForce RTX 3090 GPU (with 24 GB memory) (Nvidia, Santa Clara, CA, USA) is used for both training and testing.