Semi-Supervised Exploration in Image Retrieval

We present our solution to Landmark Image Retrieval Challenge 2019. This challenge was based on the large Google Landmarks Dataset V2[9]. The goal was to retrieve all database images containing the same landmark for every provided query image. Our solution is a combination of global and local models to form an initial KNN graph. We then use a novel extension of the recently proposed graph traversal method EGT [1] referred to as semi-supervised EGT to refine the graph and retrieve better candidates.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Menglong Zhu,et al.  Detect-To-Retrieve: Efficient Regional Aggregation for Image Search , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Giorgos Tolias,et al.  Fine-Tuning CNN Image Retrieval with No Human Annotation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[6]  Jiri Matas,et al.  Total recall II: Query expansion revisited , 2011, CVPR 2011.

[7]  Maksims Volkovs,et al.  Explore-Exploit Graph Traversal for Image Retrieval , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Bohyung Han,et al.  Large-Scale Image Retrieval with Attentive Deep Local Features , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[9]  Albert Gordo,et al.  Deep Image Retrieval: Learning Global Representations for Image Search , 2016, ECCV.