An Efficient Search on Cloud Images with Blockchain Technology

Prof. Dr. P. C. Senthilmahesh; Ms. S. Annapoorani

doi:10.32628/CSEIT124102119

Authors

Prof. Dr. P. C. Senthilmahesh M.E., Ph.D., Head of The Department of Computer Science and Engineering, Excel Engineering College, Tamilnadu, India Author
Ms. S. Annapoorani Department of Computer Science and Engineering, Excel Engineering College, Tamilnadu, India Author

DOI:

https://doi.org/10.32628/CSEIT124102119

Keywords:

Image Storage, Duplicate Detection, Blockchain Creation, Query Image Processing, Feature Extraction, Index Detection, Similarity Image Retrieval

Abstract

A key task in computer vision is image retrieval, which has wide-ranging applications across multiple domains. Using query picture features, this abstract proposes an image retrieval approach that focuses on the widely used Scale-Invariant Feature Transform (SIFT) algorithm for feature extraction and distance calculation. The suggested method starts by extracting SIFT features from a set of photos, building a keypoints and descriptor database. By capturing the unique qualities of nearby image regions, these features enable reliable matching and retrieval. The security of private cloud data, which includes query queries, the search tree, and outsourced photographs, is another major issue. Use a feature extraction method first for integrated picture features, which are composed of fundamental components like colour and shape. In particular, because the proposed method uses a balancing index tree, it can achieve logarithmic search time. Second, the picture and query feature are encrypted using the secure inner product. Include a system for determining duplicate picture material as well. When given a query image, SIFT feature extraction is applied to it, producing keypoints and descriptors that correspond to its visual characteristics. Next, a distance computation method like Euclidean distance is used to compare the features of the database photos with the query image features. The similarity between the query image and the database images is measured through this comparison. The retrieved photos are ranked in order of similarity to the query image based on the calculated distances. Search results with lesser distances between images are deemed more similar and are displayed first. To locate visually related images in huge databases, the image retrieval system that uses SIFT feature extraction and distance calculation provides a reliable and effective solution. It contributes to developments in multimedia retrieval, visual analytics, and picture understanding by enabling applications like content-based image search, image recommendation systems, and image clustering.

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