Performance evaluation of new colour histogram-based interest point detectors

• Main Authors: Rassem, Taha H., Bee, Ee Khoo
• Format: Article
• Language: English
• Published: Springer US 2015
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/6672/1/Performance_evaluation_of_new_colour_histogram-based_interest_point_detectors.pdf; http://umpir.ump.edu.my/id/eprint/6672/; https://doi.org/10.1007/s11042-014-2235-4; https://doi.org/10.1007/s11042-014-2235-4

Interest point detection is an active area in computer vision due to its importance in many applications. Measuring the pixel-wise difference between image pixel intensities is the mechanism of most detectors that have been proposed in literature. Recently, interest point detectors were proposed that incorporated the histogram representation instead of image pixel intensity. In this paper, research that extends histogram-based interest point detectors is introduced. Four colour-space representations were used to construct new detectors: HSV, Opponent, Transformed and Ohta colour spaces. Several experiments were performed to evaluate the new colour histogram-based detectors and compare them with previous detectors. First, the proposed detectors were evaluated in an image-matching task. Then, we studied and evaluated the performance of some of the local image descriptors that were extracted from the interest points and regions detected by the proposed detectors. Finally, the four top-ranked descriptors in the descriptor evaluation experiments were used to evaluate the new colour histogram-based detectors in an image-classification task using different object and scene image datasets. The experimental results demonstrate that our new detectors possess an increased ability to distinguish and more robust in regards to image matching, particularly with respect to textured scene images that involve transformations, such as illumination, viewpoint and blur changes. Furthermore, the descriptor performance may change depending on the detector and data set type. The image-classification results demonstrate that the proposed detectors exhibit higher classification accuracy for certain descriptors and data sets than the other detectors.