Robust feature detection based on local variation for image retrieval.
ABSTRACT This paper proposes an interest point detector based on wavelet transform as well as a descriptor based on image variation and log-polar coordinate. Taking advantage of the wavelet properties, the proposed method detects a small number of interest points that are distinctive and robust to the illumination changes, scale changes and affine transform. A new descriptor based on the image variation and log-polar coordinate is proposed to represent the image local shape feature without edge detection. Since the proposed descriptor groups the image variation into various levels and separates the image local region into grids based on log-polar coordinate, it overcomes the problem of textured scenes or ill-defined edge images. Experimental results show that the proposed method achieves better matching accuracy and faster matching speed than those of the SIFT, PCA-SIFT and GLOH with less interest points.
Conference Proceeding: Wavelet-based salient points for image retrieval[show abstract] [hide abstract]
ABSTRACT: The use of interest points in content-based image retrieval allows the image index to represent local properties of the image. 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 be corners and corners may gather in small regions. We present a salient point detector that extract points where variations occur in the image, whether they are corner-like or not. The detector is based on the wavelet transform to detect global variations as well as local ones. The wavelet-based salient points are evaluated for image retrieval with a retrieval system using texture features. In this experiment our method provides better retrieval performance compared with other point detectors.Image Processing, 2000. Proceedings. 2000 International Conference on; 02/2000
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ABSTRACT: We present the ALOI collection of 1,000 objects recorded under various imaging circumstances. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. These images are made publicly available for scientific research purposes.International Journal of Computer Vision 12/2004; 61(1):103-112. · 3.62 Impact Factor
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ABSTRACT: The detection of the stable local image features is one of the most critical tasks for many object recognition algorithms. The Scale Invariant Feature Transform (SIFT) has been shown to be effective to the image matching or object recognition. However, the large number of features generated by the SIFT is a disadvantage for the real time application. In this paper, we present a novel approach to detect more important local features by finding the higher information keypoints (HIKs). An input color image is firstly decomposed into an intensity image, a hue image and a saturation image. Then we detect the HIKs in these color component images in terms of the keypoint positions. Furthermore, a weight for each HIK is assigned according to the position relationship of the keypoints to improve the matching accuracy. Experiments show that the proposed approach can achieve higher matching accuracy and reduce the matching time by using the HIKs and their weights.