Locality Constrained Encoding Graph Construction and Application to Outdoor Object Classification

In this paper, we develop a new efficient graph construction algorithm that is useful for many learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent l(1) graph that is based on sparse coding, our proposed objective function has an analytical solution (based on self-representativeness of data) and thus is more efficient. This paper has two main contributions. Firstly, we introduce the Two Phase Weighted Regularized Least Square (TPWRLS) graph construction. Secondly, the obtained data graph is used, in a semi-supervised context, in order to categorize detected objects in driving/urban scenes using Local Binary Patterns as image descriptors. The experiments show that the proposed method can outperform competing methods.