Traditional content based image retrieval attempts to retrieve images using syntactic features for a query image. Annotated image banks and Google allow the use of text to retrieve images. In this paper, we studied the task of using the content of an image to retrieve information in general. We describe the significance of object identification in an information retrieval paradigm that uses ima...

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...

Rapid growth of World Wide Web has increased the interest towards image retrieval. Different groups need to find a desired image from a collection. The users may require access to the images, based on primitive features, such as color, texture or shape, or associated text. The technology to access these images has also accelerated phenomenally. The current approaches are broad and inter-discipl...

Object discovery is one of the steps to effective content-based image retrieval. If a vision system has the ability to identify potential objects in an image, it enables the user to phrase queries in terms of objects, and it enables the computer to search the image library for similar objects. This paper describes a physics-based approach to object discovery. The approach grows seed regions to ...

Traditional computer-based image retrieval techniques provide several mechanisms to retrieve images. Generally, there are two major approaches being used in image retrieval systems. The first approach is content-based image retrieval which aims to retrieve images based on their visual criteria. This approach typically utilizes visual features, such as color, shape, texture, and layout, to deter...

Most of the currently available image database systems provide a text-based retrieval function called keyword retrieval, where users specify `keywords' such as titles, attributes, and categories of themes. But many times it is not easy for users to specify suitable keywords for a particular retrieval. Besides, building a large image database with complete description of contents is a very dicu...

Despite the continuous development of features and mid-level representations, effectively and reliably measuring the similarity among images remains a challenging problem in image retrieval tasks. Once traditional measures consider only pairwise analysis, context-sensitive measures capable of exploiting the intrinsic manifold structure became indispensable for improving the retrieval performanc...

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, a...