In instance image retrieval, considering local spatial information within an image has proven effective to boost retrieval performance, as demonstrated by local visual descriptor based geometric verification. Nevertheless, it will be highly valuable to make ordinary global image representations spatial-context-aware because global representation based image retrieval is appealing thanks to its algorithmic simplicity, low memory cost, and being friendly to sophisticated data structures. To this end, we propose a novel feature learning framework for instance image retrieval, which embeds local spatial context information into the learned global feature representations. Specifically, in parallel to the visual feature branch in a CNN backbone, we design a spatial context branch that consists of two modules called online token learning and distance encoding. For each local descriptor learned in CNN, the former module is used to indicate the types of its surrounding descriptors, while their spatial distribution information is captured by the latter module. After that, the visual feature branch and the spatial context branch are fused to produce a single global feature representation per image. As experimentally demonstrated, with the spatial-context-aware characteristic, we can well improve the performance of global representation based image retrieval while maintaining all of its appealing properties. Our code is available at https://github.com/Zy-Zhang/SpCa.

Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel "base-detector repository" and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.

Identifying the discriminative characteristic of a query is important for image retrieval. For retrieval without human interaction, such characteristic is usually obtained by average query expansion (AQE) or its discriminative variant (DQE) learned from pseudo-examples online, among others. In this paper, we propose a new query expansion method to further improve the above ones. The key idea is to learn a "unique'' discriminative characteristic for each database image, in an offline manner. During retrieval, the characteristic of a query is obtained by aggregating the unique characteristics of the query-relevant images collected from an initial retrieval result. Compared with AQE which works in the original feature space, our method works in the space of the unique characteristics of database images, significantly enhancing the discriminative power of the characteristic identified for a query. Compared with DQE, our method needs neither pseudo-labeled negatives nor the online learning process, leading to more efficient retrieval and even better performance. The experimental study conducted on seven benchmark datasets verifies the considerable improvement achieved by the proposed method, and also demonstrates its application to the state-of-the-art diffusion-based image retrieval.