Vector quantization and signal compression
Vector quantization and signal compression
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
Equal-average hyperplane partitioning method for vector quantization of image data
Pattern Recognition Letters
Incremental feature weight learning and its application to a shape-based query system
Pattern Recognition Letters
Fast Invariant Feature Extraction for Image Retrieval
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
IEEE Transactions on Software Engineering
An efficient encoding algorithm for vector quantization based on subvector technique
IEEE Transactions on Image Processing
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The four most important issues in content-based image retrieval (CBIR) are how to extract features from an image, how to represent these features, how to search the images similar to the query image based on these features as fast as we can and how to perform relevance feedback. This paper mainly concerns the third problem. The traditional features such as color, shape and texture are extracted offline from all images in the database to compose a feature database, each element being a feature vector. The “linear scaling to unit variance” normalization method is used to equalize each dimension of the feature vector. A fast search method named equal-average K nearest neighbor search (EKNNS) is then used to find the first K nearest neighbors of the query feature vector as soon as possible based on the squared Euclidean distortion measure. Experimental results show that the proposed retrieval method can largely speed up the retrieval process, especially for large database and high feature vector dimension.