CAJ | 학술논문

构建视觉词典是BOVW模型中关键的一个步骤，目前大多数视觉词典是基于K-means聚类方式构建。然而由于K-means聚类的局限性以及样本空间结构的复杂性与高维性，这种方式构建的视觉词典往往区分性能较差。在谱聚类的框架下，提出一种区分性能更强的视觉词典学习算法，为了减少特征在量化过程中区分性能的降低以及谱聚类固有的存储计算问题，算法根据训练样本的类别标签对训练数据进行划分，基于Nystr?m谱聚类得到各子样本数据集的中心并得到最终的视觉词典。在Scene-15数据集上的实验结果验证了算法的正确性和有效性。特别当训练样本有限时，采用该算法生成的视觉词典性能较优。
구건시각사전시BOVW모형중관건적일개보취，목전대다수시각사전시기우K-means취류방식구건。연이유우K-means취류적국한성이급양본공간결구적복잡성여고유성，저충방식구건적시각사전왕왕구분성능교차。재보취류적광가하，제출일충구분성능경강적시각사전학습산법，위료감소특정재양화과정중구분성능적강저이급보취류고유적존저계산문제，산법근거훈련양본적유별표첨대훈련수거진행화분，기우Nystr?m보취류득도각자양본수거집적중심병득도최종적시각사전。재Scene-15수거집상적실험결과험증료산법적정학성화유효성。특별당훈련양본유한시，채용해산법생성적시각사전성능교우。
Construction of visual vocabulary is a crucial step in popular Bag-of-Visual-Words(BOVW)model. Currently, K-means clustering is generally applied to constructing the visual vocabulary. However, the visual dictionary tends to be of low discrimination due to limitation of K-means clustering and complexity of high dimensional spatial structure of samples. Under the frame of spectral clustering, a dictionary learning algorithm with stronger discriminative capability is proposed. In order to reduce degradation of descriptors discriminative power during quantization and the inherent problems of storage and calculation in spectral clustering, the training samples are divided into sub-sample sets according to the label information of category. Centers of each data set are obtained based on spectral clustering with Nystr?m algorithm and then the final compact visual dictionary is generated. Experimental results in Scene-15 dataset verify the correctness and effectiveness of the proposed algorithm. Especially when the training samples are limited, the visual dictionary via the algorithm can obtain better performance.