Spatial-content image search in complex scenes

Although the topic of image search has been heavily studied in the last two decades, many works have focused on either instance-level retrieval or semantic-level retrieval. In this work, we develop a novel visually similar spatial-semantic method, namely spatial-content image search, to search images that not only share the same spatial-semantics but also enjoy visual consistency as the query image in complex scenes. We achieve the goal by capturing spatial-semantic concepts as well as the visual representation of each concept contained in an image. Specifically, we first generate a set of bounding boxes and their category labels representing spatial-semantic constraints with YOLOV3, and then obtain visual content of each bounding box with deep features extracted from a convolutional neural network. After that, we customize a similarity computation method that evaluates the relevance between dataset images and input queries according to the developed image representations. Experimental results on two large-scale benchmark retrieval datasets with images consisting of multiple objects demonstrate that our method provides an effective way to query image databases. Our code is available at https://github.com/MaJinWakeUp/spatial-content.