Content Based Image Retrieval

This paper provides an in-depth analysis of advanced deep learning-based hashing techniques specifically designed for Content-Based Image Retrieval (CBIR) within extensive datasets. Five leading deep hashing methods are examined, assessing their effectiveness based on key performance metrics, including precision, computational cost, and scalability. To ensure a robust comparison, the experiments were conducted using well-established datasets, namely CIFAR-10 and CIFAR-100. These datasets are commonly utilized in the field of image processing and machine learning, making the findings relevant and applicable to existing research. Implementations of the original methodologies associated with each hashing technique are used for simulations, maintaining consistency in their experimental setup by executing each method under identical conditions. Additionally, to enhance the reliability of the results, the outcomes are averaged over multiple independent runs. This rigorous approach allows for a comprehensive understanding of how each deep hashing method performs, providing valuable insights into their strengths and weaknesses in the context of content-based image retrieval. The experiments lead to quantitative results concerning the performance of each technique. The findings of this evaluation contribute to the ongoing development and refinement of deep learning techniques in image retrieval applications, particularly in scenarios involving large-scale datasets.