Content-Based Image Retrieval Using Deep Feature Extraction with ResNet-50

Content-Based Image Retrieval Using Deep Feature Extraction with ResNet-50

G.Ravi, ASSISTANT PROFESSOR,

Dept of CSE-AIML ,SREENIDHI INSTITUTE OF SCIENCE AND TECHNOLOGY(SNIST),

ravi.g@sreenidhi.edu.in

Guntha Aishwarya,

Dept of CSE-AIML, ,SREENIDHI INSTITUTE OF SCIENCE AND TECHNOLOGY(SNIST),

gunthaaishwarya@gmail.com

M Spoorthi Reddy,

Dept of CSE-AIML,SREENIDHI INSTITUTE OF SCIENCE AND TECHNOLOGY(SNIST),

spoorthireddy0303@gmail.com

M Sri Chaithra,

Dept of CSE-AIML,SREENIDHI INSTITUTE OF SCIENCE AND TECHNOLOGY(SNIST),

srichaithrareddy07@gmail.com

Abstract:
This project presents a Content-Based Image Retrieval (CBIR) system that utilizes deep learning to improve the efficiency and accuracy of image similarity search. The system leverages a pre-trained ResNet-50 convolutional neural network, repurposed as a deep feature extractor by removing its final classification layers. Input images are first pre- processed and then passed through the network to extract high-dimensional feature vec- tors that capture rich visual semantics. These deep features are compared using cosine similarity to identify visually similar images. The system supports real-time image up- loads and retrieval by matching queries against a pre computed dataset of image fea- tures, enabling fast and responsive search capabilities. By replacing traditional hand- crafted features with deep feature representations, the system achieves significantly higher retrieval accuracy and robustness across various image types and domains. This approach demonstrates strong potential for practical deployment in areas such as digital asset management, visual search engines, and e-commerce product discovery, where visual similarity plays a critical role. Overall, the integration of deep learning into CBIR systems represents a significant advancement in the field of image search and retrieval.

Keywords: CBIR, Deep Learning, ResNet-50, Feature Extraction, Image Simi- larity