Saliency Weighted Convolutional Features for Instance Search
This work addresses instance search for image retrieval by improving efficiency and accuracy, though it is incremental as it builds on existing bag-of-features methods with saliency weighting.
The paper tackles instance search by weighting local convolutional features with saliency models, achieving state-of-the-art performance on the INSTRE benchmark and competitive results on Oxford and Paris benchmarks without complex region analysis or fine-tuning.
This work explores attention models to weight the contribution of local convolutional representations for the instance search task. We present a retrieval framework based on bags of local convolutional features (BLCF) that benefits from saliency weighting to build an efficient image representation. The use of human visual attention models (saliency) allows significant improvements in retrieval performance without the need to conduct region analysis or spatial verification, and without requiring any feature fine tuning. We investigate the impact of different saliency models, finding that higher performance on saliency benchmarks does not necessarily equate to improved performance when used in instance search tasks. The proposed approach outperforms the state-of-the-art on the challenging INSTRE benchmark by a large margin, and provides similar performance on the Oxford and Paris benchmarks compared to more complex methods that use off-the-shelf representations. The source code used in this project is available at https://imatge-upc.github.io/salbow/