Proceedings ArticleDOI
An image classification method based on a SK sub-vector multi-hierarchy clustering algorithm
Xianbo Lang,Guochang Gu,Hongxun Yao,Jun Ni +3 more
- pp 182-186
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TLDR
To make image databases more effectively organized, this paper presents a SK (sequence clustering plus the K-mean clustering) sub-vector multi-hierarchy clustering algorithm, effectively proven by experiments for automatic image clustering, that could organize image database efficiency so as to improve image retrieval.Abstract:
To make image databases more effectively organized, we present a SK (sequence clustering plus the K-mean clustering) sub-vector multi-hierarchy clustering algorithm in this paper. It clusters images to numbers of classes automatically, according to human perception. It utilized HSV histograms, wavelet texture features, color-texture moments, a gray gradient co-occurrence matrix, and hierarchical distribution features, to put similar semantic images into the same set. The algorithm was effectively proven by experiments for automatic image clustering, for the clustering image categories have semantically similar properties. It could organize image database efficiency so as to improve image retrieval.read more
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