In this paper we propose a novel deep spatial transformer convolutional neural network (Spatial Net) framework for the detection of salient and abnormal areas in images. The proposed method is general and has three main parts: (1) context information in the image is captured by using convolutional neural networks (CNN) to automatically learn high-level features; (2) to better adapt the CNN model to the saliency task, we redesign the feature sub-network structure to output a 6-dimensional transformation matrix for affine transformation based on the spatial transformer network. Several local features are extracted, which can effectively capture edge pixels in the salient area, meanwhile embedded into the above model to reduce the impact of highlighting background regions; (3) finally, areas of interest are detected by means of the linear combination of global and local feature information. Experimental results demonstrate that Spatial Nets obtain superior detection performance over state-of-the-art algorithms on two popular datasets, requiring less memory and computation to achieve high performance.

中文翻译：

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一种用于图像显着性检测的新型深层空间变压器卷积神经网络

在本文中，我们提出了一种新颖的深层空间变压器卷积神经网络（空间网）框架，用于检测图像中的显着和异常区域。该方法具有通用性，主要包括三个部分：（1）利用卷积神经网络（CNN）自动学习高级特征，获取图像中的上下文信息。（2）为了使CNN模型更好地适应显着性任务，我们重新设计了特征子网络结构，以基于空间变换器网络输出6维变换矩阵进行仿射变换。提取了多个局部特征，可以有效捕获显着区域中的边缘像素，同时嵌入上述模型中，以减少突出显示背景区域的影响；（3）最后，通过全局和局部特征信息的线性组合来检测感兴趣区域。实验结果表明，在两个流行的数据集上，Spatial Nets的检测性能优于最新算法，所需的内存和计算量更少，以实现高性能。