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Comparison of Image Feature Descriptors for Mobile Visual Search. Vijay Chandrasekhar David Chen Andy Lin Gabriel Takacs Sam Tsai Ngai-man Cheung Bernd Girod Stanford University. Outline. Feature-level experiments Winder & Brown data set Experiment 1: Evaluation of MPEG-7 descriptor
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Comparison of Image Feature Descriptors for Mobile Visual Search Vijay Chandrasekhar David Chen Andy Lin Gabriel Takacs Sam Tsai Ngai-man Cheung Bernd Girod Stanford University
Outline • Feature-level experiments • Winder & Brown data set • Experiment 1: Evaluation of MPEG-7 descriptor • Experiment 2: Comparison of MPEG-7, CHoG, SIFT • Image-level experiments • ZuBuD data set • Experiment 3: Pairwise image matching
… True Matches (%) [Winder & Brown CVPR ’09] False Matches (%) Feature-level Experiment Matching Pairs True Matches False Matches ROC Distance
CHoG: Compressed Histogram of Gradients Patch Gradient distributions for each bin Gradients dx dx dy dy CHoGDescriptor 011101 Spatial binning 0100101 01101 101101 Histogram Compression 0100011 111001 0010011 01100 Huffman coding Type coding Lloyd Max VQ [Chandrasekhar et al., CVPR 09,10] 1010100
Image-level Experiment 1005 database images, 115 query images Compare every query image against every database image Use ratio test followed by affine RANSAC MPEG-7 local descriptor: Hamming distance CHoG descriptor: symmetric KL divergence Database image with highest number of post-RANSAC feature matches against query image is compared against ground truth label
Experiment 3: Image Matching Results Matching Accuracy (%) Max 80 keypoints MPEG-7 descriptor MPEG-7 matching Max 80 keypoints MPEG-7 descriptor Ratio test/RANSAC Max 600 keypoints MPEG-7 descriptor Ratio test/RANSAC Max 600 points CHoG descriptor Ratio test/RANSAC
Number of feature matches post RANSAC Number of matches post RANSAC Max 600 keypoints MPEG-7 descriptor Ratio test/RANSAC Max 600 points CHoG descriptor Ratio test/RANSAC
Conclusion • CHoG outperforms MPEG-7 by significant margin • Feature-level ROC performance • Image-level matching accuracy • Number of feature matches • 80 features/query not enough