Content-based image retrieval faces a vital problem, namely “semantic gap” that exists between low level features and semantic concept. In order to solve this problem, image automatic annotations that allow users to access a large image database with textual queries are put forward. In this paper, the main study concentrates on an automatic image annotation method based on vector quantization (...

This paper aims at developing a hybrid scheme for intelligent image retrieval using neural nets. Each item in an image database is indexed by a visual feature vector, which is extracted using color moments and discrete cosine transform coefficients. Query is characterized by a set of semantic labels, which are predefined by system designers and associated with domain concerns. The proposed hybr...

A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of userprovided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficien...

Relevance feedback is a powerful technique for image retrieval and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multilevel image content model have been formulated. However, these methods only perform relevance feedback on low-level image f...

To address one of the important and challenging problems – large-scale contentbased face image retrieval. Given a query face image, content-based face image retrieval tries to find similar face images from a large image database.Large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this method, to utilize automatically detected human attribu...

In image retrieval systems, user information needs is expressed using multiple types of query. Unfortunately, due to user subjectivity perception to visual features and semantic depths of images, the conventional query submitted to the system encounter difficulties to identify user information need. The blooming of interest in semantic image retrieval requires current research direction to be m...

The semantic annotation of images can benefit from representations of useful concepts and the links between them as ontologies. Recently, several multimedia ontologies have been proposed in the literature as suitable knowledge models to bridge the well known semantic gap between low level features of image content and its high level conceptual meaning. Nevertheless, these multimedia ontologies ...

The demand for automatically annotating and retrieving medical images is growing faster than ever. In this paper, we present a novel medical image retrieval method for a special medical image retrieval problem where the images in the retrieval database can be annotated into one of the pre-defined labels. Even more, a user may query the database with an image that is close to but not exactly wha...

Due to the ‘semantic gap’ between low-level visual features and the rich semantics in user’s mind, performance of traditional contentbased image retrieval systems is far from user’s expectation. In attempt to reduce the ‘semantic gap’, this paper introduces a region-based image retrieval system with high-level semantic color names used. For each segmented region, we define a perceptual color as...

In this work we present a new interface for image retrieval system that highlights the potential of a real-time, Web-based application. This system, the Perceptually-Relevant Image Search Machine (PRISM), combines the capabilities of content-based, content-free, and semantic annotation-based image retrieval. Each of the aforementioned image retrieval methods has its own strengths, weaknesses, a...