This paper confers the evolution of the various image retrieval techniques starting from Keyword based Image Retrieval (KBIR) to Ontology Based Image Retrieval. It addresses various outstanding issues in both past and present. The rapid advancement of image data demands continuing the research and development of Image Retrieval. The exploration in image retrieval has its basis in keyword based ...

A Content Based Image Retrieval System is a computer system for browsing, searching and retrieving images from a large database of digital images .Most common methods of image retrieval utilize some method of adding meta data such as captioning, keywords or description to the images so that retrieval can be performed over the annotation words. Content Based Image Retrieval (CBIR) deals with ret...

content based image retrieval from large databases has turn into a field of broad attention these days in much application. In this paper we present new image retrieval system that employs color as image features to illustrate the content of an image region. Our work presents an evaluation of the proposed CBIR systems: CM, DM, HA, RGBC and ED. These methods boost the retrieval precision of our ...

Technology has been a very good development in the past twenty or thirty years, content-based image retrieval, many low-level visual features is proposed for image retrieval, real-time problem in image retrieval has got great attention of researchers, content-based image retrieval technique has been widely used in medical, education, digital library, industrial and commercial fields and based o...

Image retrieval is a very imperative area of digital image processing. Images can be retrieving from a large database on the basis of text, color, structure or content. Content-based image retrieval uses the visual fillings of an image such as texture, color, shape, & spatial layout to represent and index the image. In typical CBIR systems, the visual content of the pictures in the database are...

the goal of this paper is to present use of fusion techniques in content based image retrieval. this techniques improve semantic value of user queries. Hence, improve performance of content based image retrieval system. Basically, Images are expressed at different semantic levels. Content Based Image Retrieval is growing technologies for bridging the semantic gap that presently prevents deploym...

Content-Based Image Retrieval (CBIR) system is rising as a crucial analysis space wherever the users will search and retrieve pictures supported by the properties like shape, color and texture from the image information. Typically texture-based image retrieval is taken into account as a clever image of coarseness, dissimilarity and roughness however there's a lot of texture data within the edge...

We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, regionbased texture segmentation is applied on the target images prior to the actual image retrieval process. The retri...

Content-Based Image Retrieval has been a major area of research in recent years. Efficient image retrieval with high precision would require an approach which combines usage of both the color and texture features of the image. In this paper we propose a method for enhancing the capabilities of texture based feature extraction and further demonstrate the use of these enhanced texture features in...

This paper describes the participation of the SNUMedinfo team at the two retrieval tasks (Ad-hoc image-based retrieval and Case-based retrieval) in the ImageCLEF 2013 medical task. For the ad-hoc image-based retrieval task, we submitted 1 baseline textual run using query likelihood model in Indri search engine, and 4 visual runs utilizing various image features implemented in Lire image retriev...