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was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content-based searching. In this paper we survey some technical aspects of current content-based image retrieval systems based on several neural network architectures. Firstly we discuss the image retrieval system based on
integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. To overcome this limitation, several approaches that can annotate images in a semi-supervised or unsupervised way have emerged. In this paper
Content-based image retrieval systems can automatically extract visual content of images which allow users to query images by their low-level features (such as color and texture). However, users usually prefer querying images based on high-level concepts such as keywords. Classifying images into a number of categories
A neural network model with adaptive structure for image annotation is proposed in this paper. The adaptive structure enables the proposed model to utilize both global and regional visual features, as well as correlative information of annotated keywords for annotation. In order to achieve an approximate global
Existence of countless digital images has given rise to image retrieval in many applications. Conventional image databases being text-annotated pose two major problems of keywords for images and complexity. Hence, retrieval systems based on image's visual content are more desirable [1]. The content based image
img(Anaktisi) is a C#/.NET content base image retrieval application suitable for the Web. It provides efficient retrieval services for various image databases using as a query a sample image, an image sketched by the user and keywords. The image retrieval engine is powered by innovative compact and effective
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