초록
영어
In this paper, we introduce a simple but efficient method to construct powerful image representation via spatially weighted deep convolutional feature aggregation for image retrieval. First, the convolutional features are extracted from the convolutional layer of the CNNs and proposed spatial weights are applied to the convolutional features. A sum pooling is performed to aggregate the spatially weighted convolutional features into the global image representation. We carry out extensive experiments on Oxford and Paris Building datasets and experiment results show that the proposed method achieves competitive performance compared to current state-of-the-art methods.
목차
Abstract
1. Introduction
2. Related Work
2.1 Traditional image retrieval
2.2 CNN-based image retrieval
3. Spatially Weighted Convolutional Feature Aggregation
4. Experiments
4.1 Datasets
4.2. Evaluation metrics
4.4 Implementation details
4.5 Experiment results
5. Conclusion
Acknowledgement
References
1. Introduction
2. Related Work
2.1 Traditional image retrieval
2.2 CNN-based image retrieval
3. Spatially Weighted Convolutional Feature Aggregation
4. Experiments
4.1 Datasets
4.2. Evaluation metrics
4.4 Implementation details
4.5 Experiment results
5. Conclusion
Acknowledgement
References
저자정보
참고문헌
자료제공 : 네이버학술정보