MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641087012 A) filed by Vallurupalli Nageswara Rao Vignana Jyothi Institute Of Engineering And Technology on July 16, 2026, for A Deep Learning And Nlp-Based Framework For Context-Aware Image Retrieval Through Semantic Captioning And Query Understanding.
Inventors include Mrs. Somavarapu Jahnavi; B Rishab Gupta; Ch Harsha Vardhan; M Pavan Kumar; and Vaishnav Arkala.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The growing number of digital images has rendered efficient retrieval a serious issue, since conventional approaches based on low-level features like color and texture are not able to capture semantic content. Users often find it difficult to retrieve appropriate images because there is no context-aware search mechanism, particularly when precise metadata or visual information is not known. Current retrieval systems are unable to fill the semantic gap between text-based queries and image content, producing inaccurate or incomplete results. In order to solve this, we introduce a context-aware image retrieval system that combines deep learning and natural language processing (NLP) approaches. The system utilizes BLIP model for automatic image captioning, constructing a meaningful textual description of images. User requests are processed based on Sentence-BERT (SBERT) embeddings, coupled with TF-IDF and Levenshtein distance-based string matching to enable accurate retrieval. An intuitive user-friendly search is offered through a Gradio-based interface. Through this work, it is illustrated that visual feature extraction combined with NLP captioning and query processing boosts the accuracy of image search substantially. The system suggested here not only enhances the precision in retrieval but also maintains scalability and adaptability for big unstructured data. Future efforts will be dedicated to achieving further computational efficiency, including multimodal retrieval, and improving user personalization to improve the system responsiveness to various user needs.
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