B-RISiC: A Balanced-Region Indexing Approach for Efficient Reverse Image Search in Collages

The significance of digital data, specifically of digital visual information has grown immensely and driving advancements in fields like medical, entertainment, and scientific research. The domain focused on retrieving such visual content is known as Content-Based Information Retrieval (CBIR). Content-Based Image Retrieval (CBIR) encompasses two primary sub-domains: image searching and reverse image searching. The study presents a significant contribution to the field of reverse image searching. Most current research focuses on reverse searching of individual images, primarily exploring techniques for searching single images in databases. However, composite images or collages are now widely used to represent summarized visual data in fields like medicine, entertainment, and satellite imagery. Even though our prior work for RISiC (baseline method) demonstrated promising results in accuracy, it did not explicitly address the challenge of time constraints. The study addresses the gap by proposing a method that achieves a balance between accuracy and time efficiency. The approach leverages Colored Binary SIFT (CBSIFT) combined with a novel region-balanced feature extraction technique. Additionally, an Inverted Index Structure is used in the online phase, along with a feature pruning method to enhance time efficiency while maintaining high retrieval precision. The work-balanced method for Reverse Image Searching in Collages (B-RISiC) is evaluated on Caltech-101, Corel5k, and Oxford5k datasets. The performance is benchmarked against two approaches: the simple BSIFT with Indexing and the baseline method. The results indicate that B-RISiC outperformed all others, achieving search times of 1.796 s, 7.763 s, and 0.718 s for the Caltech-101, Corel5k, and Oxford5k datasets respectively, with precision values of 0.8217, 0.8575, and 0.8810.