The modern world uses digital data invariably and the advent of smart phones and cameras add to images and communicating through images in social media is a very common happening due to the exposure to internet and the economical availability of data. Moreover, people explore the web for much news and on different topics. They feel satisfied to look into image type info than textual information. So, image retrieval is paramount nowadays and it is very much useful in many societal and defence applications. The methods used for retrieval started through text based and query based is popularly used now. The content based retrieval uses many stages, approaches, technologies, algorithms etc. It is intended to provide a broad perspective about CBIR in this work. Moreover, the computational and time complexities involved during retrieval relies on the performance of the entire system. One of the aspects that can improve the precision is the reduction of semantic gap. Taking this as the base, we have explored a literature and presented the points related to various points including relevance feedback so as to enable other researchers to carry out experimental work on the suggested areas.

现代世界总是使用数字数据，由于暴露于互联网和经济的数据可用性，智能手机和相机的出现增加了图像，并且通过社交媒体中的图像进行通信是非常普遍的现象。此外，人们在网络上浏览大量新闻和不同主题。他们对查找图像类型信息而不是文本信息感到满意。因此，如今图像检索至关重要，并且在许多社会和国防应用中非常有用。通过基于文本和基于查询的检索方法现已广泛使用。基于内容的检索使用许多阶段，方法，技术，算法等。旨在为这项工作提供有关CBIR的广阔视野。此外，检索过程中涉及的计算和时间复杂度取决于整个系统的性能。可以提高精度的方面之一是减少语义鸿沟。以此为基础，我们探索了文献，并提出了与包括相关性反馈在内的各个方面相关的观点，以使其他研究人员能够在建议的领域进行实验工作。