Bag-of-features signature using invariant region descriptor for object retrieval

In recent years, a content-based method such as `bag-of-features´ (BoF) is coming to the fore as an object recognition and classification technique. This paper proposes a BoF signature using invariant region descriptor for object retrieval. The region descriptors are extracted from dense sampled regions in the training images. These descriptors are quantized by hierarchical k-means clustering in a vocabulary tree of visual words. Each image is represented by occurrence of visual words, and then we use linear combination distance measure in the matching. In the experiments, we use object images that are taken in different condition to evaluate the retrieval performance. The test results show that the proposed method outperforms the BoF method using SURF descriptor. The proposed method searches 2.9 correct images among 3 on average up to the top 3% rank in database. Therefore, the proposed method is considered as an effective technique in terms of retrieval accuracy.