Higher-order Co-occurrence Features based on Discriminative Co-clusters for Image Classification
Takumi Kobayashi · 2012
We propose a method to extract image features based on effective higher-order cooccurrences. The proposed method constructs the co-clusters to discriminatively quantize joint primitive quantitative data, such as pair-wise pixel intensities, unlike the standard co-occurrence methods that utilize simple clusters trained in an unsupervised manner for quantizing point-wise data. The discriminative co-clusters effectively exploit the co-occurrence characteristics even by a fewer number of cluster components, resulting in low-dimensional co-occurrence features. By taking advantage of those discriminative co-clusters, the co-occurrence features can be extended to the higher-order co-occurrence features of feasible dimensionality. The higher-order co-occurrence captures richer information in image textures by extracting relationships in multiplets more than only doublets (pairs). In the experiments on image classifications for cancer cells and pedestrians, the proposed method exhibits favorable performances compared to the other methods, even to the standard co-occurrence based methods.