【摘要】 场景识别是图像高层语义信息理解的重点和难点领域。如何寻找场景中有效信息的位置是场景识别领域中非常困难的问题。该文提出了一种基于多尺度显著区域特征学习的场景识别方法。首先,提取一个场景中在多尺度下的显著区域;然后,通过卷积神经网络的迁移学习,利用学习到的特征在多尺度的显著区域内对场景进行识别。基于两个公共场景识别数据库上的实验证明了该方法的有效性和良好的泛化能力。实验结果表明,该方法相对于传统的场景识别方法能取得更好的场景识别准确度。更多还原

【Abstract】 Scene recognition is an important and challenging topic in the research filed of high level image understanding. Traditional researches of scene recognition focused on handcrafted features, which result in limited discriminative and generalization ability. In addition, finding regions in a scene with rich information is always very challenging. This paper presents an effective method for scene recognition based on learned features from multi-scale salient regions. The method first finds multi-scale salient regions in a scene and then extracts the features from the regions via transfer learning using convolutional neural networks(Conv Nets). Experiments on two popular scene recognition datasets show that our proposed method is effective and has good generalization ability for scene recognition, compared with the benchmarks on both of the datasets.更多还原