The Design of the Context Quantizer on the basis of Amazing Measure
Yiping Zhang, Min Chen
Available Online June 2015.
- https://doi.org/10.2991/meici-15.2015.176How to use a DOI?
- Context modeling; Context quantization; Amazing measure; Clustering algorithm
- When using the clustering algorithm, the implementation of the context quantization not only expands the application range of the quantizer, but also obtains better coding performance. However, those clustering algorithms depend on the choice of the similarity measure. In the the previous works, the increment of the description length was suggested but the result is that it cannot fully meets the similarity measure of various attributes, resulting in the performance of the clustering result deviation. In this paper, a new similarity measure which holds better mathematical description is given. The increment of the amazing measure, which denotes the similarity measure, two count vectors are discussed in this paper and its corresponding properties are also explained. The experimental results indicate that when using the proposed similarity measure, both the stability of the context quantizer and the corresponding coding results can be optimized at the same time.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yiping Zhang AU - Min Chen PY - 2015/06 DA - 2015/06 TI - The Design of the Context Quantizer on the basis of Amazing Measure BT - 2015 International Conference on Management, Education, Information and Control PB - Atlantis Press SP - 1013 EP - 1016 SN - 1951-6851 UR - https://doi.org/10.2991/meici-15.2015.176 DO - https://doi.org/10.2991/meici-15.2015.176 ID - Zhang2015/06 ER -