Content-based Image Clustering via Multi-view Visual Vocabula
Content-based image clustering is a challenging but useful topic for the efficient management of image databases and effective image retrievals especially with the emerging of huge number of images on the websites and in our common life. Thanks to the big success of bag of words (BOW) model in the field of text mining, visual vocabulary composed of bag of visual words(BOVW) is employed to the field of content-based image processing and analysis (including image clustering) in recent years. In practice, a single visual vocabulary usually leads to the irregular partition for image database due to the instability of the random initialization in general clustering algorithms such as K-Means and due to the lack of semantic meanings in visual words, To circumvent these drawbacks, a new image clustering strategy based on multiple visual vocabularies is proposed in this paper, which can provide the multi-view information from the given image database. This new strategy is based on a tensor method named multi-linear singular value decomposition (MLSVD), which can leverage the effect of each view to facilitate the clustering procedure. The experiments on the subset of Caltech 101 image database show that this strategy can obtain the robust and even better clustering results by integrating multi-view information.
Content-based image clustering Visual vocabulary Bag of visual words Spectral clustering MLSVD
XU Wangming LIU Xinhai FANG Kangling
College of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3974-3977
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）