Text-Guided Unsupervised Hashing With Community Exploration for Image Retrieval

Hashing retrieval plays a crucial role in image retrieval tasks due to its efficiency and accuracy. In singlemodal hashing-based image retrieval, the core challenge lies in enabling the network to fully understand the image content, thereby promoting more effective feature extraction. Inspired by cross-modal learning paradigms, we observe that augmenting images with captions or descriptive text provides rich contextual information that enhances the network's understanding of visual content. Incorporating textual descriptions into image representations helps overcome the limitations of relying solely on visual features, such as incomplete information or interpretive bias, which leads to more robust feature extraction. However, unresolved problems remain, including how to obtain and utilize text semantics when the dataset contains only images and how to effectively partition images to explore global structures without supervision. In this paper, we propose a novel Text-Guided Unsupervised Hashing (TGUH) method with community exploration. TGUH incorporates textual semantic representations into singlemodal image retrieval through multimodal information fusion, facilitating a deeper understanding of image content within the network. Additionally, we design an unsupervised dynamic community partitioning approach to adaptively partition image communities. This allows for the construction of hash centers under unsupervised conditions. Experimental results demonstrate that our method achieves significant improvements in retrieval accuracy, highlighting its practical advantages and potential for real-world applications.