Won-Keun Yang

Very fast concentric circle partition-based replica detection method

Image replica detection becomes very active research field recently as the electronic device such as the digital camera which generates digital images spreads out rapidly. As huge amount of digital images leads to severe problems like copyright protection, the necessity of replica detection system gets more and more attention. In this paper, we propose a new fast image replica detector based on concentric circle partition method. The proposed algorithm partitions image into concentric circle with fixed angle from image center position outwards. From these partitioned regions, total of four features are extracted. They are average intensity distribution and its difference, symmetrical difference distribution and circular difference distribution in bitstring type. To evaluate the performance of the proposed method, pairwise independence test and accuracy test are applied. We compare the duplicate detection performance of the proposed algorithm with that of the MPEG-7 visual descriptors. From experimental results, we can tell that the proposed method shows very high matching speed and high accuracy on the detection of replicas which go through many modification from the original. Because we use the hash code as the image signature, the matching process needs very short computation time. And the proposed method shows 97.6% accuracy on average under 1 part per million false positive rate.

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.