Evaluation of Salient Point Techniques
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Query by image and video content: a colored-based stochastic model approach
Data & Knowledge Engineering
Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach
Applied Soft Computing
Content-based image retrieval using visually significant point features
Fuzzy Sets and Systems
Stable wave detector of blobs in images
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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In image retrieval, global features related to color or texture are commonly used to describe the image. The use of interest points in content-based image retrieval allows image index to represent local properties of images. Classic corner detectors can be used for this purpose. However, they have drawbacks when applied to various natural images for image retrieval, because visual features need not to be corners and corners may gather in small regions. We present a salient point detector that extracts points where variations occur in the image, regardless whether they are corner-like or not. It is based on wavelet transform to detect global variations as well as local ones. We show that extracting the color information in the locations given by these points provides significantly improved retrieval results as compared to the global color feature approach. We also show an image retrieval experiment based on texture features where our detector provides better retrieval performance comparing with other point detectors.