An object-oriented approach for semantic-based image retrieval is presented. The goal is to identify key patterns of specific objects in the training data and to use them as object signature. Two important aspects of semantic-based image retrieval are considered: retrieval of images containing a given semantic concept and fusion of different low-level features. The proposed approach splits the ...

Structured knowledge models, such as semantic hierarchies and ontologies, appear to be a way to improve the accuracy of automatic image annotation. It allows modeling many valuable semantic relations between concepts based on image annotation using contextual and spatial relationships. Indeed, these relationships have been proved to be of prime importance for the understanding of image semantic...

Social networks have become popular due to its photo sharing facilities. People are interested to explore contents that contain images. Since internet has become a part of life people are interested in uploading images in it. Hence with the exponentially growing photos, large-scale content-based face image retrieval is a facilitating technology for many emerging applications. In this paper, our...

In the last decade, digital imaging has experienced a worldwide revolution of growth in both the number of users and the range of applications. The amount of digital image content produced on a daily basis is still increasing drastically. As from the very beginning of photography, those who took pictures tried to capture as much information as possible about the photograph and in today's digita...

This project addresses content-based image retrieval in general, and in particular, focuses on developing a hidden class detection methodology to address effective semantics-intensive image retrieval. In our approach, each image in the database is segmented into classes and contains classified images. We explore the query adaptive ranking to retrieve images. With this representation, model base...

With increasingly large image databases, searching in them becomes an ever more difficult endeavor. Consequently, there is a need for advanced tools for image retrieval in a webscale context. Searching by tags becomes intractable in such scenarios as large numbers of images will correspond to queries such as “car and house and street”. We present a novel approach that allows a user to search fo...

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems....

The semantic gap is often regarded as a major problem in the eld of image retrieval research. In this paper, I will show that there are other important topics that should be addressed for improving the image retrieval utility. Among them, the exploitation of limited information and motivating the use of images are considered to be central to the development of image retrieval.

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...

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...