基于自学习对比度视觉注意模型和自适应深度特征的无分类目标检测

项目名称： 基于自学习对比度视觉注意模型和自适应深度特征的无分类目标检测

项目编号： No.61501407

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 钱晓亮

作者单位： 郑州轻工业学院

项目金额： 19万元

中文摘要： 无分类目标检测致力于定位图像中的所有前景目标，可以有效减少后续任务的搜索范围从而减少计算量。针对现有无分类目标检测方法存在的问题，结合前期的研究基础，本项目主要研究以下内容：针对现有方法大量使用弱标签样本（已知包含目标的类型而未标注具体位置）带来的定位精度不足的问题，发展一种基于自学习对比度的视觉注意模型对弱标签样本进行自动标注；针对现有方法使用同一模型进行特征提取带来的泛化能力不足问题，研究一种基于堆叠降噪自动编码机的自适应深度特征提取方法，和现有的基于卷积神经网络的特征提取方法联合进行特征提取；针对现有方法使用单一策略（采用样本特征训练分类器）构建无分类目标检测系统带来的可靠性不足的问题，提出一种融合视觉显著性的目标属性测量和空间位置修正模型，并据此构建无分类目标的在线检测系统。本项目的研究成果预计能显著提高前景目标的定位精度，在特定类目标检测，图像压缩等领域具有重要的应用价值。

中文关键词： 无分类目标检测；视觉注意；自学习对比度；自适应特征；自动标注

英文摘要： The category-independent object detection is committed to localizing all of the foreground objects, which can save the computing resource by reducing the searching region. Several key problems will be resolved in this project based on the applicant’s previous research work. The main research contents of this project are as follows. First of all, the object localization of existing methods is coarse because of the utilization of weak-label samples. Consequently, a visual attention model based on self-learning contrast is proposed for automatic labeling of weak-label samples. Secondly, the generalization capability of existing methods is insufficient because the same model is applied to all of images for feature extraction. To resolve this problem, an adaptive deep feature extraction model based on stack denoising autoencoder is proposed and combined with the current feature extraction model based on convolutional neural network for final feature extraction. Finally, the reliability of existing methods is insufficient because only a single strategy in which the classifier is trained by the features is considered. To remedy this defect, an objectness measuring model and spatial coordinate fixation model in which the visual saliency is incorporated are designed and used to construct the real-time category-independent object detection system. This project is expected to significantly improve the precision of localization of foreground objects, which has important application value for category-specific object detection, image compression, etc.

英文关键词： category-independent object detection;visual attention;self-learning contrast;adaptive features;automatic labeling