In this paper we propose a novel method to bridge the 'semantic gap' between a user's information need and the image content. The semantic gap describes the major deficiency of content-based image retrieval (CBIR) systems which use visual features extracted from images to describe the images. We conquer the deficiency by extracting semantic of an image from the environmental texts around it. Since an image generally co-exists with accompany texts in various formats, we may rely on such environmental texts to discover the semantic of the image. A text mining approach based on self-organizing maps is used to extract the semantic of an image from its environmental texts. We performed experiments on a small set of images and obtained promising results.
We achieved content-based image retrieval by using the shape information contained in a image. A kind of high-order image features which emulate the feature detection process of human eyes were used to represent both input image and the template images stored in a database. Template matching was then applied between the input image and each template image to obtain the retrieval result. The matching process performs a kind of pseudo elastic matching between the feature sets of the input image and each template image. Such elastic matching process, together with the high-order features, provides an excellent approach to measure the dissimilarity, namely the spatial topology distance, between images. The method had been tested on the Columbia Object Image Library database. Preliminary experiments suggested promising result by our approach.
The printed areas of a handprinted character with thick strokes were replaced by a frame formed by bended ellipses to represent the character efficiently and emulate the high order receptive fields in later visual system. Each bended ellipse maximally fits the local stroke pattern and captures the position, orientation and topology information contained in the local stroke pattern. Complex stroke structures are represented by concept neurons which each contains several bended ellipses. The craft of concept neurons provides an uniform representation for receptive fields in any order. The model uses these concept neurons in searching their corresponding neurons in the template frame. To obtain the correspondence, a global affine transform followed by a local distorting process are used to align the two frames. To afford topology preservation the topology order of the character is generated explicitly. The classification is achieved by examining the similarity between the topology order of the handprinted pattern and the template patterns.
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