Advancements in Content-Based Image Retrieval: A Comprehensive Survey of
  Relevance Feedback Techniques
Hamed Qazanfari Mohammad M. AlyanNezhadi Zohreh Nozari Khoshdaregi
Content-based image retrieval (CBIR) systems have emerged as crucial tools in
the field of computer vision, allowing for image search based on visual content
rather than relying solely on metadata. This survey paper presents a
comprehensive overview of CBIR, emphasizing its role in object detection and
its potential to identify and retrieve visually similar images based on content
features. Challenges faced by CBIR systems, including the semantic gap and
scalability, are discussed, along with potential solutions. It elaborates on
the semantic gap, which arises from the disparity between low-level features
and high-level semantic concepts, and explores approaches to bridge this gap.
One notable solution is the integration of relevance feedback (RF), empowering
users to provide feedback on retrieved images and refine search results
iteratively. The survey encompasses long-term and short-term learning
approaches that leverage RF for enhanced CBIR accuracy and relevance. These
methods focus on weight optimization and the utilization of active learning
algorithms to select samples for training classifiers. Furthermore, the paper
investigates machine learning techniques and the utilization of deep learning
and convolutional neural networks to enhance CBIR performance. This survey
paper plays a significant role in advancing the understanding of CBIR and RF
techniques. It guides researchers and practitioners in comprehending existing
methodologies, challenges, and potential solutions while fostering knowledge
dissemination and identifying research gaps. By addressing future research
directions, it sets the stage for advancements in CBIR that will enhance
retrieval accuracy, usability, and effectiveness in various application
domains.