Bi-Modal Content Based Image Retrieval using Multi-class Cycle-GAN

Content Based Image Retrieval (CBIR) systems retrieve relevant images from a database based on the content of the query. Most CBIR systems take a query image as input and retrieve similar images from a gallery, based on the global features (such as texture, shape, and color) extracted from an image. There are several ways of querying from an image database for retrieval purpose. Some of which are text, image, and sketch. However, the traditional methodologies support only one of the domains at a time. There is a need of bridging the gap between different domains (sketch and image) for enabling a Multi-Modal CBIR system. In this work, we propose a novel bimodal query based retrieval framework, which can take inputs from both sketch and image domains. The proposed framework aims at reducing the domain gap by learning a mapping function using Generative Adversarial Networks (GANs) and supervised deep domain adaptation techniques. Extensive experimentation and comparison with several baselines on two popular sketch datasets (Sketchy and TU-Berlin) show the effectiveness of our proposed framework.