1 / 15

Learning Visual Similarity Measures for Comparing Never Seen Objects

This paper discusses the purpose, methodology, and results of learning visual similarity measures for comparing never seen objects, specifically in the context of object recognition. The authors present a method for training and testing images using corresponding patch pairs and quantizing patch pair similarity measures. The paper also explores feature extraction, model database, matching, hypothesis generation, and hypothesis verification techniques. Results are presented for toy cars, Ferencz, faces, and Coil 100 reference datasets.

randellm
Download Presentation

Learning Visual Similarity Measures for Comparing Never Seen Objects

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Learning Visual Similarity Measures for Comparing Never Seen Objects By: Eric Nowark, Frederic Juric Presented by: Khoa Tran

  2. Outline • 1.) Purpose • 2.) Methodology • 3.) Results

  3. Purpose Object Recognition Train Images Test Images

  4. Methodology Preview A.) Corresponding patch pair B.) Quantizing patch pair C.) Patch pair similarity measure

  5. Object Recognition • 1.) Images • 2.) Feature Extraction • 3.) Model Database • 4.) Matching • a.) Hypothesis Generation • b.) Hypothesis Verification Images Features Extraction Model Database Matching Hypothesis Generation Hypothesis Verification

  6. Images • Total: - 225 images, - 21 different objects • Training Data Set - 1185 positive image pairs - 7330 negative image pairs - 14 different objects • Testing Data Set - 1044 positive image pairs - 6337 negative image pairs - 7 different objects

  7. Feature Extraction • Patches • Normalized Cross Correlation • SIFT Descriptors • Matrix representation

  8. Model Database • Extremely Randomized Binary Decision Tree • SIFT Descriptors • Geometric Information • Information Gain

  9. Model Database – SIFT Descriptors

  10. Model Database

  11. Hypothesis Generation – Similar Measure • Similar Measure • Support Vector Machine

  12. Hypothesis Generation Ferencz and Malik Faces in the News Dataset Dataset

  13. C.) Hypothesis Verification • Sammon mapping for toy cars

  14. Results 1.) Toy Cars 2.) Ferencz 3.) Faces 4.) Coil 100

  15. Reference • Eric Nowak and Fredric Jurie; "Learning Visual Similarity Measures for Comparing Never Seen Objects” ;Computer Vision and Pattern Recognition 2007 (CVPR'07);

More Related