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KantoCV/Selective Search for Object Recognition

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第29回関東CV勉強会「有名論文読み会」
J. Uijlings et al., “Selective Search for Object Recognition,” IJCV 2013

Published in: Engineering
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KantoCV/Selective Search for Object Recognition

  1. 1. Selective Search for Object Recognition J. Uijlings et al., IJCV 2013 2015.5.30 #cvsaisentan @sakanazensen
  2. 2. Self introduction • @sakanazensen(魚の人) • 某英語なIT企業の2年目戦士 • 名古屋大M卒 – Semi-supervisedな歩行者検出 – 元nagoyacvの中の人 • 生息範囲:CVやAgileなど
  3. 3. 今日紹介する論文 • Selective Search for Object Recognition • Jasper R. R. Uijlingsら(アムステルダム大学) • International Journal of Computer Vision (2013) • Segmentation As Selective Search for Object Recognition • ICCV2011 • オリジナル
  4. 4. • Exhaustive Searchが一般的 画像からの物体検出 Classifier 25,132win/imgはつらい (640x480/OpenCVデフォルト) detection window
  5. 5. やりたいこと • 何かありげな領域を推定 • 後続の処理はそこだけに集中できる! • より高度な識別(分類)手法が適用できる! ✓class-independent ✓high-recall ✓任意の位置・大きさ ✓高効率 “Object Hypotheses”
  6. 6. 従来の似た研究 • どちらかと言うとSegmentationの研究 • Carreiraら[1]、Endresら[2] • 前景背景のセグメンテーション • “Objectness”[3] • 学習ベース+ランダム探索 [1] J. Carreira et at;, “Constrained parametric min-cuts for automatic object segmentation,” CVPR, 2010 [2] I. Endres et al., ”Category independent object proposals,” ECCV, 2010 [3] B. Alexe et al., “What is an object?,” CVPR, 2010
  7. 7. この研究のアプローチ • ボトムアップ型の階層的セグメンテーション • あらゆる位置・スケール(+構造)に対応 • セグメンテーション結果からObject Hypotheses生成
  8. 8. 主要なReferrer • DCNNベース物体検出などの前段に利用[4] • また、Objectness[3]と共によく引用されてる印象 [4] R. Girshick et al., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” CVPR2014
  9. 9. Selective Search
  10. 10. Selective Search全体の流れ 一つになるまで 結合繰り返し initial segmentation 類似度の高い 隣接領域を結合 …
  11. 11. Selective Search全体の流れ … 各層各小領域の外接矩形を Object hypothesesとする
  12. 12. Selective Search全体の流れ (1) 初期セグメンテーション𝑅 = 𝑟1, … , 𝑟𝑛 (2a) 各小領域の特徴𝐹 = {𝑓1, … , 𝑓𝑛} (2b) 隣接小領域の類似度𝑆 = {𝑠 𝑟𝑖, 𝑟𝑗 , … } (3) while 𝑆 ≠ 𝜙 • 𝑠(𝑟𝑖, 𝑟𝑗) = max(𝑆) • 𝑟𝑡 = 𝑟𝑖⋃𝑟𝑗 • 𝑆から𝑟𝑖と𝑟𝑗に絡むものを全て消す • 𝑆 = 𝑆 ∪ 𝑠(𝑟𝑡, 𝑟𝑥 , … }, 𝑅 = 𝑅 ∪ 𝑟𝑡 (4) 𝑅からObject hypothesesを生成
  13. 13. 1. 初期セグメンテーション𝑅 • Efficient Graph-Based Image Segmentation[5] • 高速! • (他手法でもOK) 𝑘 = 50 𝑘 = 200 𝑘 = 500 [5] P. Felzenszwalb et al, “Efficient Graph-Based Image Segmentation”, IJCV2004
  14. 14. • 色特徴 • テクスチャ特徴 • 小領域の面積 • 小領域の外接矩形 2a. 各小領域の特徴𝐹抽出 𝑓𝑖 𝑓𝑗 𝑓𝑘 … 𝐹 = 𝑓1, … , 𝑓𝑛
  15. 15. 2a. 特徴量の設計 • ミソ:線形性(加法性) • 最初に求めるだけでOK! |𝑟1|𝑓1⨁|𝑟2|𝑓2 𝑟1 + 𝑟2 = 𝑓1,2 𝑟1 𝑟2 𝑟1,2 陽に求めなくていい!
  16. 16. 2b. 特徴量と類似度𝑆 • 色特徴(正規化色ヒストグラム) • 25次元×3チャネル=75次元 • 類似度𝑆color: intersection • マージ: 線形和 • テクスチャ特徴 • SIFT descriptorのような正規化勾配ヒストグラム • 80次元×3チャネル=240次元※ • 類似度𝑆texture: intersection • マージ: 線形和 ※8方向☓10binとのこと。原著には詳細の言及なし
  17. 17. 2b. 特徴量と類似度𝑆 • 面積 • スカラ • 類似度𝑆size = 1 − 𝑟 𝑖 +|𝑟 𝑗| |img| • 外接矩形 • 4次元 • 類似度𝑆fill = 1 − |boundingbox(𝑟 𝑖∪𝑟 𝑗)|− 𝑟 𝑖 −|𝑟 𝑗| |img| highlow highlow 包含関係にある領域を 積極的に統合する 小さい領域を 先に統合させる
  18. 18. 3.小領域のGreedyな統合 やるだけ! • 類似度順に取ってきては結合・類似度再計算 https://goo.gl/tZGnkb
  19. 19. 4. Object hypothesesの生成 𝑅内の全要素の外接矩形 各階層・各小領域の 外接矩形を算出 = • Object hypotheses = 小領域の外接矩形
  20. 20. 4. Object hypothesesの生成 • 数が多い&ゴミも多い 重み付き乱択で捨てる 𝑟𝑘の重み𝑣 𝑘 = rand 0, 1 × 𝑟𝑘の深さ deeper layer shallower layer
  21. 21. 4. Object hypothesesの生成 • 数が多い&ゴミも多い 重み付き乱択で捨てる 重みでソートして 上から一定数採用 (´ºωº` )えっ、それでいいんだ… Hypothesisの質と量のtrade-offに落とし込める!
  22. 22. Diversification いろんなパラメータでやりまくる(!) 色空間 類似度 𝑘 # Fast HSV, Lab 𝑆color, 𝑆texture, 𝑆size, 𝑆fill , 𝑆texture, 𝑆size, 𝑆fill 50, 100 8通り Quality HSV, Lab, rgI, H, I 𝑆color, 𝑆texture, 𝑆size, 𝑆fill , 𝑆texture, 𝑆size, 𝑆fill , 𝑆fill , 𝑆size 50, 100, 150, 300 80通り • 結果が偏らないよう、多様なObject hypotheses が必要…
  23. 23. Output • 各パラメータ組合せでの出力を統合 • ゴミも含んでいます • けど物体らしきものはなるべくカバーします
  24. 24. Detection/Recognition への応用例
  25. 25. 例:一般的な学習 Ground Truth SVM Classifier Pos Neg FPs=Hard Negs Negativeは(通常) ランダムに切り出し Try detection Retrain
  26. 26. 例:Selective Searchを活用して学習 Ground Truth SVM Classifier Pos Try detection (based on object hypotheses) FPs=Hard Negs Selective Search Pos領域に少し重なる hypothesesを切り出す Neg Retrain
  27. 27. Experiments
  28. 28. Object hypothesisの例 • Best Overwrap(真値とのAND面積比) .874 .884 .863 .882 .873
  29. 29. Object hypothesisの評価 • 評価指標 Recall 0~1 出てきてほしい物体総数のうち 実際出せた割合 MABO Mean Average Best Overwrap 0~1 クラスごとのBest Overwrapの平均 (Recallの連続版的な雰囲気)
  30. 30. Hypothesesが多いと良くなる
  31. 31. Diversificationすると良くなる いろんなパラメータでやりまくる! 実際の効果は # combination # hypotheses MABO Time Single 1パターン 362 .693 0.71s Fast 8パターン 2,147 .799 3.79s Quality 80パターン 10,108 .878 17.15s
  32. 32. 多クラスObject Recognition精度評価 • ILSVRC2011 (Localization) [6] ※Xerox Research Centre Europeの誤植? Method Error ratio (Flat) Error ratio (Hierarchical) Proposed .425 .285 ISI Lab (University of Tokyo)※ .565 .410 [6] Olga Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” arXiv:1409.0575, 2014
  33. 33. 原著でのその他の実験 • 各特徴量/色空間の寄与 • 階層的処理の効果 • 物体クラスごとのObject hypothesisの評価 • …
  34. 34. 実装してみた!
  35. 35. • オリジナル実装(ICCV版+IJCV版) • http://koen.me/research/selectivesearch/ • MATLAB • pcodeばっかりで読めない…… • 魚実装 • ほぼPure python • fork it! belltailjp/selective_search_py.git
  36. 36. ✓任意のパラメータ組合せ ✓Object hypotheses数の変更 ✓並列処理
  37. 37. 個人的なイメージ • 意外と力技 • 多様な結果を出したい→よろしいならば80通りのパラメータだ • Object Hypothesisの切り捨ての辺りは工夫の余地ありそう? • 手法はシンプル・明快で実装も容易 • 速度的には検出ガチ勢にはまだ辛い • 10−1~101sec/imgのオーダー

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