Abstract
Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.
Highlights
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphone, and Internet. e shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem [1,2,3]. e fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic relationship with the query given by the user
For getting good results between storage space, retrieval accuracy, and speed, 72 color and orientation quantization levels are used in MSD and 6 for image retrieval. e average retrieval and recall ratios of MSD are compared with other methods like Gabor MTH on Corel datasets because these algorithms are developed for image retrieval for the evaluation of MSD and the results show that our proposed model (MSD) outperforms other models
We have presented a comprehensive literature review on different techniques for content-based image retrieval (CBIR) and image representation
Summary
Due to recent development in technology, there is an increase in the usage of digital cameras, smartphone, and Internet. e shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem [1,2,3]. e fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic relationship with the query given by the user. Most of the search engines on the Internet retrieve the images on the basis of text-based approaches that require captions as input [4,5,6]. E second approach for image retrieval and analysis is to apply an automatic image annotation system that can label image on the basis of image contents. In CBIR, low-level visual features (e.g., color, shape, texture, and spatial layout) are computed from the query and matching of these features is performed to sort the output [1]. Query-By-Image Content (QBIC) and SIMPLicity are the examples of image retrieval models that are based on the extraction of low-level visual semantic [1].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
