Abstract. How to economically cluster large-scale multi-view images is
a long-standing problem in computer vision. To tackle this challenge, we
introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image
clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations
are learned by exploiting both sharable and individual information across
multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective
discrete optimization under ?21-norm constraint. By this means, heavy
continuous-valued Euclidean distance computations can be successfully
reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work
specifically designed for scalable multi-view image clustering. Extensive
experimental results on four large-scale image datasets show that HSIC
consistently outperforms the state-of-the-art approaches, whilst signifi-
cantly reducing computational time and memory footprint.