Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/4036
Title: Spatial histogram of keypoints
Authors: Tsapatsoulis, Nicolas 
Theodosiou, Zenonas 
Keywords: Local features
Hilbert space-filling curve
Spatial Histogram
Visual models creation
Issue Date: 2013
Publisher: IEEE
Source: 20th IEEE International Conference on Image Processing, 2013, Melbourne, Australia, 15-18 September
Abstract: Among a variety of feature extraction approaches, special attention has been given to the SIFT algorithm which delivers good results for many applications. However, the non fixed and huge dimensionality of the extracted SIFT feature vector cause certain limitations when it is used in machine learning frameworks. In this paper, we introduce Spatial Histogram of Keypoints (SHiK), which keeps the spatial information of localized keypoints, on an effort to overcome this limitation. The proposed technique partitions the image into a fixed number of ordered sub-regions based on the Hilbert space- filling curve and counts the localized keypoints found inside each sub-region. The resulting spatial histogram is a compact and discriminative low-level feature vector that shows significantly improved performance on classification tasks. The proposed method achieves high accuracy on different datasets and performs significantly better on scene datasets compared to the Spatial Pyramid Matching method.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/4036
DOI: 10.1109/ICIP.2013.6738602
Rights: © IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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