Download presentation

Presentation is loading. Please wait.

Published byRussell Savery Modified over 2 years ago

1
Relevance Feedback for the Earth Mover‘s Distance / 21 I9 CHAIR OF COMPUTER SCIENCE 9 DATA MANAGEMENT AND EXPLORATION Relevance Feedback for the Earth Mover‘s Distance Marc Wichterich, Christian Beecks, Martin Sundermeyer, Thomas Seidl Data Management and Data Exploration Group RWTH Aachen University, Germany

2
Relevance Feedback for the Earth Mover‘s Distance / 21 Introduction Distance-based Adaptable Similarity Search Similarity of objects defined by distance function Small distance → similar, large distance → dissimilar Query by example: user-given object, find similar ones Query and distance only approximate descriptions of user’s desired result If delivered result does not meet expectations: Bad query? Bad distance? Bad database? How to do it better? Relevance Feedback attempts to adapt query/similarity model based on simple user input (result relevancy) 1

3
Relevance Feedback for the Earth Mover‘s Distance / 21 2 Relevance Feedback user DB query, feedback results feedback system similarity model Photo: Flickr / Caro WallisCaro Wallis Earth Mover’s Distance RF for EMD RF EMD

4
Relevance Feedback for the Earth Mover‘s Distance / 21 Overview Introduction Adaptive Similarity Model Feature Signatures The Earth Mover’s Distance Relevance Feedback for the Earth Mover’s Distance Experimental Evaluation Conclusion 3

5
Relevance Feedback for the Earth Mover‘s Distance / 21 Similarity Model – Feature Signatures 4 x y color

6
Relevance Feedback for the Earth Mover‘s Distance / 21 Similarity Model – Earth Mover’s Distance Introduced in Computer Vision by Rubner et al. Used in many differing application domains Idea: transform features of Q into features of P EMD: minimum of transformation cost 5 Q P x y x y

7
Relevance Feedback for the Earth Mover‘s Distance / 21 Feature Transformation 6

8
Relevance Feedback for the Earth Mover‘s Distance / 21 EMD – Formal Definition Modeled as linear optimization (transportation problem) 7

9
Relevance Feedback for the Earth Mover‘s Distance / 21 Overview Introduction Adaptive Similarity Model Relevance Feedback for the Earth Mover’s Distance The Feedback Loop Query Adaptation Heuristic EMD Adaptation Optimization-based EMD Adaptation Experimental Evaluation Conclusion 8

10
Relevance Feedback for the Earth Mover‘s Distance / 21 The Feedback Loop 9 user DB query, feedback results feedback system similarity model yes exit start get query adapt distance no get feedback adapt query retrieve results display results satisfied?

11
Relevance Feedback for the Earth Mover‘s Distance / 21 Query Adaptation Input: signatures from relevant objects Output: new query signature Idea: cluster signature elements Refinements by Rubner: Only keep clusters with elements from majority of signatures Reweight resulting signature accordingly Combine with fixed gd L2 and call it „Query-by-Refinement“ „Query-by-Refinement“ is baseline for our evaluation We adapt EMD via ground distance 10 satisfie d? retrieve results exit start get query query distance feedback display results

12
Relevance Feedback for the Earth Mover‘s Distance / 21 Heuristic EMD Adaptation 1 Approach: pick gd based on feedback gd should reflect user preferences: Don’t care if blue cluster at upper half of image is moved left/right Do care if it is moved vertically Use variance information in relevant feedback Low variance → assume user cares High variance → assume user does not care Measure variance in feedback locally around query signature elements c i (Q). Define gd: c (Q) x FS → R ( ) 11 satisfie d? retrieve results exit start get query query distance feedback display results

13
Relevance Feedback for the Earth Mover‘s Distance / 21 Heuristic EMD Adaptation 2 Not 1 but m distance functions: gd i (c i (Q),y) = ((c i (Q) - y) V i (c i (Q) - y) T ) ½ Weighted Euclidean Distances (weights on diagonal of V i ) V i : inverted variance for c i (Q) per feature space dimension 12 satisfie d? retrieve results exit start get query query distance feedback display results

14
Relevance Feedback for the Earth Mover‘s Distance / 21 Optimization-Based EMD Adaptation 1 Aim: Pick best possible gd. Failback: Find a good one. Q: When is gd good? A: If ranking it produces is good. New Q: When is a ranking of DB good? Given ground truth, a number of measures exist We used “average precision at relevant positions” We have ground truth for part of the DB: feedback Idea: test candidates for gd on feedback 13 satisfie d? retrieve results exit start get query query distance feedback display results RankingAvg. Precision 1 1 1 1 0 0 0 01.000 1 1 0 1 0 1 0 00.854 0 0 0 0 1 1 1 10.365

15
Relevance Feedback for the Earth Mover‘s Distance / 21 Optimization-Based EMD Adaptation 2 Optimization: Optimization variable: gd Objective function: avgPrec(EMD gd, q, Feedback) Constraints: m weighted Euclidean distances Analytic optimization with closed form for weights infeasible (ranking/sorting, EMDs in objective function) Probabilistic optimization via Simulated Annealing Start with some initial solution Move in solution space Compute objective function Adopt solution with certain probability Iterate & turn more greedy 14 satisfie d? retrieve results exit start get query query distance feedback display results

16
Relevance Feedback for the Earth Mover‘s Distance / 21 Optimization-Based EMD Adaptation 3 Optimization for EMD based on Feedback: Solution: weights for m weighted Euclidean distances Initial solution: given by heuristic Moving: redistribute weights per Euclidean distance Objective function: avgPrec(EMD gd, q, Feedback) Results for EMD gd on DB? 15 satisfie d? retrieve results exit start get query query distance feedback display results

17
Relevance Feedback for the Earth Mover‘s Distance / 21 Overview Introduction Adaptive Similarity Model Relevance Feedback for the Earth Mover’s Distance Experimental Evaluation Conclusion 16

18
Relevance Feedback for the Earth Mover‘s Distance / 21 Experimental Evaluation: Databases 17 72,000 images in ALOI DB~60,000 images in COREL DB

19
Relevance Feedback for the Earth Mover‘s Distance / 21 Experimental Evaluation: ALOI 18 Heuristic Adaptation Optimization-based Query-by-Refinement

20
Relevance Feedback for the Earth Mover‘s Distance / 21 Experimental Evaluation: COREL 19 Heuristic Adaptation Optimization-based Query-by-Refinement

21
Relevance Feedback for the Earth Mover‘s Distance / 21 Experimental Evaluation 20 After 5 iterations of looking for doors in COREL: (a) Query-by-Refinement (b) Heuristic (c) Optimization-Based pos 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

22
Relevance Feedback for the Earth Mover‘s Distance / 21 Conclusion Exploited adaptability of the EMD in RF framework Goal: Improve similarity search results Techniques: Baseline: fixed ground distance Statistics-based heuristic adaptation Optimization-based adaptation Evaluation: Experiments on two image datasets More relevant objects in fewer iterations Techniques extensible to other adaptable distance functions 21

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google