STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
3-D Object Recognition Using Bipartite Matching Embedded in Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Combinatorics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Color-Spatial Indexing and Retrieval Scheme for Image Database
ICPADS '00 Proceedings of the Seventh International Conference on Parallel and Distributed Systems: Workshops
Approximate max flow on small depth networks
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
PicToSeek: combining color and shape invariant features for image retrieval
IEEE Transactions on Image Processing
Hierarchical color image region segmentation for content-based image retrieval system
IEEE Transactions on Image Processing
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Studies have been actively undertaken on image retrieval from a large-capacity image database, particularly on image retrievals such as natural image retrieval, character image retrieval or trademark retrieval from a streaming image database. In retrieving a character image, either color or shape information is used for the key feature information. However, changes in the shape of a character image often makes it difficult to retrieve solely based on the shape information, requiring a new retrieval method where both color and shape information are taken into consideration. We present a highly effective method for retrieving or matching similar character images even when shape information differs substantially. In our approach, combined features of color and shape information are used for image retrieval; an image is first split into sectors; color information is extracted from the sector images followed by quantization using Parzen window to extract features; an image is then retrieved by means of bipartite matching using the features. Our results show the retrieval rate using the combined information increases substantially for matching natural or character images compared with the results obtained by the combination of two features independently.