Introduction: Content Based Image Retrieval (CBIR) is a method of image searching and retrieval in a  database. In medical applications, CBIR is a tool used by physicians to compare the previous and current  medical images associated with patients pathological conditions. As the volume of pictorial information  stored in medical image databases is in progress, efficient image indexing and retri...

Although content-based image retrieval has been studied for decades, most commercial image search engines are still text-based. However, there is a growing demand for techniques to support content-based image search at Web scale. In this paper, we propose an ambitious goal to searching one billion Web images by content, and discuss the major challenges and opportunities. We also present several...

Our world is dominated by visual information and a tremendous amount of such information is being added day-by-day. It would be impossible to cope with this explosion of visual data, unless the images are organized such that we can retrieve them efficiently and effectively. At the core of content-based image retrieval (CBIR) is the requirement that database elements must be indexed to facilitat...

One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The disclosure of the content of these millions of photos available on the internet is of great importance. The objective of image retrieval is to allow users to efficiently browse throug...

Content-based image retrieval systems were introduced as an alternative to avoid the need of manual tagging in traditional keyword-based image retrieval systems. However, the representation of image using visual features only involves a loss of information which is referred to as semantic gap. A number of techniques have been proposed to deal with ‘semantic gap’. This paper reviews existing app...

Many models have been proposed for vision and language tasks, especially the image-text retrieval task. State-of-the-art (SOTA) in this challenge contain hundreds of millions parameters. They also were pretrained on large external datasets that proven to significantly improve overall performance. However, it is not easy propose a new model with novel architecture intensively train massive datas...

The increasing prevalence of broadband Internet access is making it easier to obtain rich contents like images, and more people are attempting image retrieval. We focus on how to present web image retrieval results to users. Most retrieval results contain multiple topics. To offset this complexity, many papers have discussed text retrieval result clustering [11][14]. In result clustering, we cl...

Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...

For its first participation in the ImageCLEF campaign, the LIC2M participated in both the ad hoc task and the medical task. Using our cross-language information retrieval system and content-based image retrieval system, our goal was to perform some first experiments on merging the results of the two systems. The results show that the performance of each system highly depends on the corpus and t...

Expansion of content-based image retrieval (CBIR) system has become a vital research issue as the digital image libraries and multimedia databases are increasing day by day. The image retrieval is divided into two major types: Text-based image retrieval and Content-based image retrieval. Conventional text-based image search engines apply manual annotation of images whereas the content-based ima...