【作者】 周鸽；

【导师】 王加俊；

【作者基本信息】 苏州大学 ， 通信与信息系统， 2011， 硕士

【摘要】 作为图像检索、图像识别、图像过滤等方法的关键技术,基于内容的图像分类技术已成为模式识别领域中的一个重要研究方向,它的目的是将图像数据按照自身的语义特征进行分类,“词袋”模型在基于内容的图像分类领域中取得了很大的成功,因此越来越受到大家的重视。但是,在构建视觉词汇表的过程中,当前的很多方法只是简单的将底层特征进行聚类,并没有考虑图像区域之间的空间关系,这导致了词汇表不够准确和稳定。本论文引入和改进了几种能够结合空间信息的算法,用于构建视觉词汇表。本文的主要贡献在以下几点:首先,提出了一种演化SOM-SD算法对传统的SOM-SD的神经网络算法进行加速并用来进行图像分类。传统的SOM-SD算法的最大优点是它能够有效处理结构数据,区分相似度较高的对象。但是,由于引入了空间信息,SOM-SD计算量非常大,影响了其在大规模图像库上的应用。在保留SOM-SD处理结构数据能力的前提下,本文利用分层演化思想提高计算效率。实验证明:演化SOM-SD算法在图像分类性能上比没有考虑结构信息的传统算法有了明显的提高,其计算速度远远高于传统的SOM-SD算法。其次,提出了一种基于空间约束的分层模糊C均值算法,该算法是基于FCM-S(基于空间约束的模糊C均值算法)改进而来的。相比K均值,其避免了噪声对视觉词汇的影响,增加算法聚类的鲁棒性;相比FCM-S,其提高了算法的计算效率。在相同环境下的实验证明,该算法在图像分类的鲁棒性和计算效率方面都有了明显的提高。更多还原

【Abstract】 Content based image categorization, as a key technique of image retrieval, image recognition and image filtering, has become one of the most important research areas in the field of the pattern recognition. It aims at classifying images into different semantic categories. The bag of visual words model which has achieved a lot of success in image classification attracts more and more attention. However most existing approaches construct a visual vocabulary by simply clustering image regions represented with low-level visual features, where spatial context of image regions has not been well utilized. This thesis adopted and improved some methods which can take the spatial context into consideration. The main contributions of this thesis are as follows:Firstly, a new algorithm called evolving SOM-SD is proposed for the acceleration of the conventional SOM-SD for image classification. The most important advantage of the conventional SOM-SD is that it can deal with structural data and distinguish similar objects. However, it is not suitable for large database because of the extremely intensive computing task resulted from the consideration of the spatial information. We resolved the problem while keeping the capability of dealing with structural data by utilizing a hierarchical and evolving strategy. Experimental results demonstrated that our proposed method performs better than those without considering spatial context and can implement much faster than the conventional SOM-SD algorithm.Secondly, an algorithm called FCM-HS is proposed for the improvement of the FCM-S (FCM with spatial constraints). It’s more robust to the noise compared with the k-means algorithm, and more efficient compared with the FCM-S algorithm. Experimental in the same environment showed a significant improvement in the robustness and efficiency in image classification.更多还原