Yuhong Xiong

A practical DCT based blind image watermarking scheme for print-and-scan process

Blind image watermarking technologies allow information to be embedded in common digital images and then recover from the watermarked images without the original images. However, the embedded information is often damaged after the print-and-scan process, because of the randomly added noises, the altered color, and the rotation and scaling introduced in the process. In this paper, we present a practical blind image watermarking scheme based on DCT domain which can survive from the print-and-scan process. The image is partitioned into blocks, and each block embeds one bit watermark data. Two uncorrelated pseudo random sequences are used to spread bit 0 and 1 in the middle frequency band of block-DCT spectrum respectively, which is done by adding the corresponding pseudo random sequence to the middle frequency block-DCT coefficients adaptively. The embedded bit is recovered by comparing the correlations of the modified middle frequency coefficients with each pseudo random sequence. Experiments show that the bit error ratio of watermarking is 2.26% after the print-and-scan process, which is robust enough for visual objects embedding. The robustness of the embedded data can be further improved by incorporating data error correction coding and data repetition voting techniques. In conclusion, this scheme achieves a good performance of both watermark robustness and watermark transparency for the print-and-scan process.

Using weighted tagging to facilitate enterprise search

Motivated by the success of social tagging in web communities, this paper proposes a novel document tagging method more suitable for the enterprise environment, named weighted tagging. The method allows users to tag a document with weighted tags which are then used as an additional source for the query matching and relevance scoring to improve the search results. The method enables a user-driven search result ranking by adapting the relevance score of a search result through weighted tags based on user feedbacks. A prototype intranet search system has been built to demonstrate the viability of the method.

A case study on rule-based and CRF-based author extraction methods

Information extraction (IE) is the task of automatically extracting structured information from unstructured documents. A typical application of IE is to process a set of documents written in a natural language and populate a database with the information extracted. This paper presents a case study on author extraction from unstructured documents. A rulebased method and a CRF-based (Conditional Random Field) method are implemented for this task. The rule-based method involves defining a set of heuristic rules and leveraging prior knowledge on author names and affiliations to identify metadata. The CRF-based method involves preparing a labeled training dataset, defining a set of feature functions, learning a CRF model, and applying the model to label new documents. We evaluate and compare the performance of the two methods through experiments, and give some useful hints for application developers on the choice of heuristics and formal methods when addressing the real-world information extraction problems.