Sujatha D, Subramaniam M, Rene Robin Chinnanadar Ramachandran
Information Technology, St. Peter's College of Engineering and Technology, Chennai, India.
Dept. of Information Technology, SreeVidyanikethan Engineering College, Tirupati, 517 102 India.
Multimed Syst. 2022;28(3):1039-1058. doi: 10.1007/s00530-022-00897-8. Epub 2022 Feb 5.
Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches. Multimedia big data consists of the most significant features with different types of data. Even though the multimedia supports various data formats with corresponding storage frameworks, similar semantic information is expressed by the multimedia. The overlap of semantic features is most efficient for theory and research related to semantic memory. Correspondingly, in recent years, deep multimodal hashing gets more attention owing to the efficient performance of huge-scale multimedia retrieval applications. On the other hand, the deep multimodal hashing has limited efforts for exploring the complex multilevel semantic structure. The main intention of this proposal is to develop enhanced deep multimedia big data retrieval with the Adaptive Semantic Similarity Function (A-SSF). The proposed model of this research covers several phases "(a) Data collection, (b) deep feature extraction, (c) semantic feature selection and (d) adaptive similarity function for retrieval. The two main processes of multimedia big data retrieval are training and testing. Once after collecting the dataset involved with video, text, images, and audio, the training phase starts. Here, the deep semantic feature extraction is performed by the Convolutional Neural Network (CNN), which is again subjected to the semantic feature selection process by the new hybrid algorithm termed Spider Monkey-Deer Hunting Optimization Algorithm (SM-DHOA). The final optimal semantic features are stored in the feature library. During testing, selected semantic features are added to the map-reduce framework in the Hadoop environment for handling the big data, thus ensuring the proper big data distribution. Here, the main contribution termed A-SSF is introduced to compute the correlation between the multimedia semantics of the testing data and training data, thus retrieving the data with minimum similarity. Extensive experiments on benchmark multimodal datasets demonstrate that the proposed method can outperform the state-of-the-art performance for all types of data.
如今，多媒体大数据在社交网络、交通、健康和电子商务等各种应用中呈指数级增长。在大规模数据集中访问首选数据需要高效且复杂的检索方法。多媒体大数据由具有不同类型数据的最重要特征组成。尽管多媒体通过相应的存储框架支持各种数据格式，但多媒体表达的是相似的语义信息。语义特征的重叠对于与语义记忆相关的理论和研究最为有效。相应地，近年来，深度多模态哈希由于在大规模多媒体检索应用中的高效性能而受到更多关注。另一方面，深度多模态哈希在探索复杂的多层次语义结构方面的努力有限。本提案的主要目的是利用自适应语义相似性函数（A-SSF）开发增强型深度多媒体大数据检索。本研究提出的模型涵盖几个阶段：（a）数据收集，（b）深度特征提取，（c）语义特征选择，以及（d）用于检索的自适应相似性函数。多媒体大数据检索的两个主要过程是训练和测试。一旦收集了涉及视频、文本、图像和音频的数据集，训练阶段就开始了。在这里，深度语义特征提取由卷积神经网络（CNN）执行，该网络又通过称为蜘蛛猴 - 猎鹿优化算法（SM-DHOA）的新混合算法进行语义特征选择过程。最终的最优语义特征存储在特征库中。在测试期间，将选定的语义特征添加到Hadoop环境中的映射 - 归约框架中以处理大数据，从而确保适当的大数据分布。在这里，引入了主要贡献A-SSF来计算测试数据和训练数据的多媒体语义之间的相关性，从而以最小的相似度检索数据。在基准多模态数据集上进行的大量实验表明，所提出的方法在所有类型的数据上都能优于当前的先进性能。
