Metadata Embedding for Vector Maps by Using Reversible Steganographic Algorithms
| Autor: | Fu-Mei Chen, 陳富美 |
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| Rok vydání: | 2009 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 97 A vector map consists of a sequence of two-dimensional coordinates to represent points, lines, and polygons in a digital map. They are data widely used in economic, social and environmental decision support and planning applications and are also the fuel of many applications based on Geographic Information Systems (GIS). Nowadays, more and more vector maps have been compiled and made available for dissemination via internet. Accordingly, there is need for providing the details of the downloaded vector maps to be available on demand. Metadata which is the “data about data” are introduced to provide the details of vector maps. In this thesis, we have explored an important issue on metadata embedding for vector maps by using reversible steganographic algorithms. The basic idea of this research comes from utilizing the characteristics of steganographic technologies to develop metadata embedding methods for vector maps. Thus, the major objective of this research is to propose and compare methods of using reversible steganographic algorithms to embed metadata in vector maps and to provide a better metadata storing mechanism than current used. Experiments are implemented to evaluate the feasibility of the proposed methods. In this thesis, we have successfully explored and proposed three reversible steganographic algorithms to embed metadata in vector maps. The first algorithm, which is named as the original algorithm, is used to embed 2(n-2) bits of metadata in a vector map, where n represents the total vertices in a vector map. To the best of our knowledge, the algorithm has achieved the highest bit per vertex (BPV) in th literature of steganograhy for vector maps. The second algorithm, which is named as the extended algorithm, is improved from the original algorithm for the purpose of decreasing the distortion of stego vector maps and increasing the accuracy of recovery vector maps. The experimental results, compare with the results from the original algorithm, show that the extended algorithm has reduced 50%-60% of distortion rate in stego vector maps and improved 40%-60% of accuracy in recovery vector maps. The third algorithm, which is named as the extensive algorithm, is proposed to have better data embedding capacity. The algorithm can be used to embed 2(n-2)s bits of metadata in a vector map. The n in the third algorithm also represents the total vertices of vector maps and the s here represents the segmentation values that create sub-intervals between the intervals designed for metadata embedding. Results show that we have successfully implementing a cover vector map with 65,828 vertices by using the extensive reversible steganographic algorithm to embed and extract metadata with insignificant distortion in stego vector maps and high accuracy of recovery vector maps. Although our approaches have already delivered good results, the main limitations of the proposed algorithms are coming from map precision and machine precision errors when considering cover vector map with small amount of vertices. Since the definitive capacity limit is reached when map precision and machine precision errors occur. Thus, the first suggested future work is to use other approaches to divide intervals or even use different approaches and rules to decide intervals for increased capacity or to avoid map precision and machine precision errors. The second future work which is worth to be investigated is to survey the effects of cover vector maps’ features to the algorithms proposed in this thesis, such as the complexity, the smoothness of boundary, and the included angle between vertices of cover maps. Finally, it is also worth to survey how to apply these algorithms in online mapping systems for providing better spatial vector data services. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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