Scalar quantization for large scale image search

By Wen-gang Zhou, Yijuan Lu, Houqiang Li, Qi Tian. 2012

96 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Lingxi Xie
2. Liang Zheng
3. Qi Tian
4. Richang Hong
5. Yi Yang

In Computer Vision and Pattern Recognition

1. Yi Yang
2. Qi Tian
3. Liang Zheng
4. Lingxi Xie
5. Dacheng Tao
6. Alan L. Yuille
7. Luc Van Gool
8. Xiaogang Wang

Citations in the corpus

• Recent advances in convolutional neural networks (2015, neutral): “, residual quantization [153], scalar quantization [154]).”
• Image Classification and Retrieval are ONE (2015, methodology): “For example, ONE requires nearly 5 × 10 11 floating operations for searching one query among one million candidates, while a simple BoVW-based approach often needs no more than 10 9 [34][73].”
• SIFT Meets CNN: A Decade Survey of Instance Retrieval (2016, methodology): “To further accelerate quantization, scalar quantization [75] suggests that local features be quantized without an explicitly trained codebook.”