Evolution of Information Retrieval System: Critical Review of Multimedia Information Retrieval System Based On Content, Context, and Concept

In recent years the explosive growth of information affects the flood of information. The amount of information must be followed by the development of the effective Information Retrieval System (IRS) so that the information will be easily accessible and useful for the user. The source of Information contains various media format, beside text there is also image, audio, and video that called multimedia. A large number of multimedia information rise the Multimedia Information Retrieval System (MIRS). Most of MIRS today is monolithic or only using one media format like Google1 for text search, tineye2 for image search, youtube3 for video search or 4shared4 for music and audio search. There is a need of information in any kind of media, not only retrieve the document in text format, but also retrieve the document in an image, audio and video format at once from any kind media format of the query. This study reviews the evolution of IRS, regress from text-based to concept- based MIRS. Unified Multimedia Indexing technique is discussed along with Concept-based MIRS. This critical review concludes that the evolution of IRS follows three paces: content-based, context-based and concept-based. Each pace takes on indexing system and retrieval techniques to optimize information retrieved. The challenge is how to come up with a retrieval technique that can process unified MIRS in order to retrieve optimally the relevant document

Enhancing Automatic Annotation for Optimal Image Retrieval

Image search and retrieval based on content is very cumbersome task particularly when the image database is large. The accuracy of the retrieval as well as the processing speed are two important measures used for assessing and comparing the effectiveness of various systems. Text retrieval is more mature and advanced than image content retrieval. In this dissertation, the focus is on converting image content into text tags that can be easily searched using standard search engines where the size and speed issues of the database have been already dealt with. Therefore, image tagging becomes an essential tool for image retrieval from large image databases. Automation of image tagging has received considerable attention by many researchers in recent years. The optimal goal of image description is to automatically annotate images with tags that semantically represent the image content. The speed and accuracy of Image retrieval from large databases are few of the important domains that can benefit from automatic tagging. In this work, several state of the art image classification and image tagging techniques are reviewed. We propose a new self-learning multilayered tagging framework that can address the limitations of current approaches and provide mutual accuracy improvement between the recognition layer and the annotation layer. Our results indicate that the proposed framework can improve the overall accuracy of information retrieval in a variety of image databases

Non-parametric Kernel Ranking Approach for Social Image Retrieval

National Research Foundation (NRF) Singapore; Ministry of Education, Singapore under its Academic Research Funding Tier

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples