An image retrieval system can be defined as searching, browsing, and retrieving images from massive databases consisting of digital images. Although Conventional and common techniques of retrieving images make use of adding metadata namely captioning keywords so as to perform annotation of words. However image search can be described by dedicated technique of search which is mostly used to find images. For searching images user provides the query image and the system returns the image similar to that of query image.
Image Retrieval Architecture
Image Retrieval has been adopted in most of the major search engines, including Google, Yahoo!, Bing, etc. A large number of image search engines mainly employ the surrounding texts around the images and the image names to index the images. Because there are only two main places where anyone can place text first in title (Name of image) and second in the tags which are proposed and implemented using web 2.0 concepts? Most of the time user make query in the text format for search contents over any search engine.
Figure 1: General Image Retrieval System
However, this limits the capability of the search engines in retrieving the semantically related images using a given query. On the other hand, the current contemporary in content-based image retrieval is rolling; it has not yet succeeded in bridging the semantic gap between human concepts, e.g., keyword-based queries, and low-level visual features that are extracted from the images. Hence, it has become an urgent need for developing novel and effective methods and techniques by which we fill the gap between the image contents and user query that go beyond these conventional approaches or retrieval models.
Recently, with the popularity of Web 2.0 applications and social media, more and more users contribute numerous tags to Web images, such as Flicker, ESP games, etc. These tags provide the meaningful descriptors of images, which are especially important for those images containing little or no textual context. The success of Flicker proves that users are willing to provide this semantic context through manual annotations. Recent user reveals that users do annotate their photos with the motivation to make them better accessible to the general public.
Figure 2: Image Retrieval Approaches
With the development of the Internet, and the availability of image capturing devices such as digital cameras, huge amounts of images are being created every day in different areas including remote sensing, fashion, crime prevention, publishing, medicine, architecture, etc. For this purpose, the need for the development of efficient and effective methodologies to manage large image databases for retrieval is urgent so many general- purpose image retrieval systems have been developed. There are three methods for image retrieval: text-based method, content-based method and hybrid method. This section explains in details each method. Image retrieval system can be classified as:
- Text based Image retrieval system
- Content Based Image retrieval system
Applications of Image Retrieval
Image Retrieval has been used in several applications, such as medicine, fingerprint identification, biodiversity information systems, digital libraries, crime prevention, historical research, among others:
Medical Applications: The number of medical images produced by digital devices has increased more and more. For instance, a medium-sized hospital usually performs procedures that generate medical images that require hundreds or even thousands of gigabytes within a small space of time.
Biodiversity Information: Systems Biologists gather many kinds of data for biodiversity studies, including spatial data, and images of living beings. Ideally, Biodiversity Information Systems (BIS) should help researchers to enhance or complete their knowledge and understanding about species and their habitats by combining textual, image content-based, and geographical queries
Digital Libraries: There are several digital libraries that support services based on image content. One example is the digital museum of butterflies, aimed at building a digital collection of Taiwanese butterflies. This digital library includes a module responsible for content-based image retrieval based on color, texture, and patterns.
 Datta, Ritendra, Dhiraj Joshi, Jia Li, and James Z. Wang, “Image retrieval: Ideas, influences, and trends of the new age”, ACM Computing Surveys (Csur) 40, Number. 2 (2008): 5.
 John P. Eakins, “Towards intelligent image retrieval”, Pattern Recognition 35 (2002), pp. 3-14
 Priyanka Shinde and Shrinivas Halhalli, “Image Retrieval Based on its Contents Using Features Extraction” (2016)
 João Augusto da Silva Júnior and Rodiney Elias Marçal, “Image Retrieval: Importance and Applications”, In Workshop de Vis~ ao Computacional-WVC, 2014.