這是12/09(二) 資訊工程研討的演講，歡迎聽講！

演講者: Tyng-Luh Liu, Research Fellow of the Institute of Information Science at Academia Sinica

Taipei, Taiwan

時間: 12/09(二) 13:20~15:10

地點: 綜合一館地下室 101室 (AB101)

主持人：王昱舜教授

主題: Efficient Binary Codes for Extremely High-        dimensional Data

大綱:

Recent advances in tackling large-scale computer vision problems have supported the use of an extremely high-dimensional descriptor to encode the image data. Under such a setting, we focus on how to efficiently carry out similarity search via employing binary codes. Observe that most of the popular high-dimensional descriptors induce feature vectors that have an implicit 2-D structure. We exploit this property to reduce the computation cost and high complexity. Specifically, our method generalizes the Iterative Quantization (ITQ) framework to handle extremely high-dimensional data in two steps. First, we restrict the dimensionality-reduction projection to a block-diagonal form and decide it by independently solving several moderate-size PCA sub-problems. Second, we replace the full rotation in ITQ with a bilinear rotation to improve the efficiency both in training and testing. Our experimental results on a large-scale dataset and comparisons with a state-of-the-art technique are promising.

簡介:

Tyng-Luh Liu received his PhD in Computer Science from New York University (NYU) in 1997. He is currently a research fellow of the Institute of Information Science at Academia Sinica. His research interests include computer vision, pattern recognition, and machine learning.

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