Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network
Indoor versus Outdoor Scene Classification Using Probabilistic Neural Network
复制标题
使用概率神经网络进行室内与室外场景分类
DOI：
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发表时间：
2007
影响因子：
1.9
通讯作者：
Sukhendu Das
中科院分区：
文献类型：
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作者：
L. Gupta;V. Pathangay;A. Patra;A. Dyana;Sukhendu Das
我们提出了一种方法，室内与室外场景分类使用概率神经网络（PNN）。最初使用模糊均值聚类（FCM）和基于颜色，纹理和形状的特征从每个图像段中提取的场景分割（无监督）。因此，图像由特征集表示，每个图像片段具有单独的特征向量。由于片段的数量从一个场景到另一个场景不同，因此场景的特征集表示具有不同的维度。因此，一个修改后的PNN用于分类的变维特征集。所提出的技术进行评估两个数据库：IITM-SCID 2（场景分类图像数据库）和佩恩和辛格在2005年使用。不同的功能组合的性能进行了比较，使用修改后的PNN。
We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy-means clustering (FCM) and features based on color, texture, and shape are extracted from each of the image segments. The image is thus represented by a feature set, with a separate feature vector for each image segment. As the number of segments differs from one scene to another, the feature set representation of the scene is of varying dimension. Therefore a modified PNN is used for classifying the variable dimension feature sets. The proposed technique is evaluated on two databases: IITM-SCID2 (scene classification image database) and that used by Payne and Singh in 2005. The performance of different feature combinations is compared using the modified PNN.