02411nas a2200217   4500000000100000000000100001008004100002260001200043653001900055653001500074653002600089653002000115653002300135100001800158245012700176856008000303300001200383490000600395520177800401022001402179       2023                            d  c06/202310aCross Indexing10aSimilarity10aHierarchical Skeleton10aImage Retrieval10aSimilarity Measure1 aZhong Qianwen00aCosine Similarity Based Hierarchical Skeleton and Cross Indexing for Large Scale Image Retrieval Using Mapreduce Framework  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_11.pdf  a108-1220 v83 aThe imaging data in various fields like industries, institutions, medical, and so on has grown exponentially in recent years. An innovative software solution is required for the efficient management of image data. The MapReduce framework is used for large-scale image data processing. Various cross-indexing techniques are developed to transform the image into binary sequences but retrieving the image from the reducer on the feature vector results in a major challenge. Image retrieval using large-scale image databases attained major attention, where cross-indexing plays a key role in the research community. Therefore, in this research, a new method for image retrieval, named Cosine Similarity-based hierarchical skeleton and cross-indexing, is proposed to perform the retrieval process in the MapReduce framework effectively. The feature vector of the images is converted to binary sequences. The Most Significant Bit (MSB) of the binary code is used to store the images in the mapper using the cross-indexing model. The image retrieval process is achieved through the reducer based on the tanimoto similarity measure. The binary sequence for the query image is calculated based on the feature vector. The MSB bit of the binary code is matched with the MSB code of the images in
the mapper to achieve the retrieval process. The proposed method effectively achieved better performance through the cross-indexing model with the usage of the feature vector. The performance of the proposed method is compared with the existing techniques using the UK bench dataset. The proposed method attains the values of 0.784, 0.729, 0.75, 31.23, 17.84secfor F1-score, precision, recall, computational cost, and computational time with the query set-1 by considering four mappers.  a1989-1660