We propose a novel descriptor for complex object matching and classification. The proposed descriptor combines both local and global properties of interest points. The proposed descriptor is highly distinctive and insensitive to common image transformations. Adaptive object distance is proposed to measure the similarity between objects. We compare our approach with many existing methods and we achieve the state-of-the-art performances on several benchmarks.