JP5011287B2 - 視覚量から生成された複数の顕著性マップから一つの顕著性マップを得る方法 - Google Patents
視覚量から生成された複数の顕著性マップから一つの顕著性マップを得る方法 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims description 21
- 230000000007 visual effect Effects 0.000 title claims description 20
- 238000010606 normalization Methods 0.000 claims description 10
- 239000000284 extract Substances 0.000 claims 2
- 238000005259 measurement Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 3
- 230000003935 attention Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 230000001427 coherent effect Effects 0.000 description 1
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- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
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- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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Description
・固定領域のコントラスト測定値は、ランダムに取った領域のコントラスト測定値よりも高い。換言すれば、ある領域のコントラストは、その性質(ルミナンス、色、動き、テクスチャなど)に関わりなく、我々の注目を引きつける。たとえその領域が観察者によって実行されようとしているタスクに関係ないときでさえそうである。
・相関測定に基づいて、これらの研究はまた、固定領域がその近傍と異なることを示している。
CS(s)=N(N(CSA(s))+N(CSCr1(s))+N(CSCr2(s)))
ここで、CSA(s)は無色成分の顕著性マップを表し、CSCr1は第一の色成分の顕著性マップを表し、CSCr2は第二の色成分の顕著性マップを表す。
・この方法は、準一様な分布をもつ顕著性マップと一つまたは複数の顕著性ピークをもつ顕著性マップの間の区別をしない;
・顕著性マップにいくつかの顕著性ピークが存在するとき、この種のマージは明らかに最高の顕著性ピークを優遇する;
・この方法はインパルスノイズに非常に敏感である;
・マップ間の相互作用がない。
・前記複数の顕著性マップを各視覚量の理論的な最大に基づいて規格化する段階と、
・各顕著性マップにおける主たる顕著性領域を選択するマップ内競合(competition)段階と、
・前記諸マップ内競合と、前記諸マップ内競合とある部位が前記複数の顕著性マップ上に現れる確率との積の関数であるマップ間冗長性項との和に基づくマップ間競合段階とを有する。
・マップの最も有意な領域を識別するマップ内競合;
・種々のマップの冗長性と相補性を活用したマップ間競合。マップ間冗長性の使用は、ある種の領域がいくつかの次元での顕著性を生成するときに、該ある種の領域の顕著性を補強する方法である。逆に、ある領域が一つの視覚的次元においてのみ顕著性を生成するときには、マップ間相補性を使うことが必要である。
・前記複数の顕著性マップの諸部位は、視覚量の値の理論的な最大に対して規格化され、
・前記複数のマップは、所定の数のレベルを使って線形に量子化される。
・視覚量の値が大きくなる諸部位を含む、顕著であると言われる諸部位を含む予備的リストが定義され、
・顕著であると言われる前記諸部位の前記予備的リストが前記値の降順にスキャンされ、顕著であると言われる前記部位を中心とする所定の半径の円形領域内に存在する顕著であると言われるその他の部位が禁止され、
・次に大きい値の部位の値との比が所定の閾値より大きい非禁止部位を含む、前記諸顕著部位のリストが確立される。
IntraMapCr1(s)=CSNQ Cr1(s)/NearestMaxcr1(s)
IntraMapCr2(s)=CSNQ Cr2(s)/NearestMaxcr2(s)
関数NearestMaxCr1(またNearestMaxcr2)は、部位sに最も近い成分Cr1(またCr2)の極大値を返す。この値は、サイズがK1(またK2)個の値のリストL1(またL2)から取られる。リストのサイズは、極大nと極大n+1との間の比が、ある閾値より大きくなるように決定される。閾値は任意に1.3に設定される。これは、主たる顕著性領域のみを考慮することを可能にする。
Intermap(s)=complementarity(s)+redundancy(s)
項complementarity(s)〔相補性〕は、マップ内競合の結果どうしを足し合わせることによって得られる:
complementarity(s)=intraMapCr1(s)+intraMapCr2(s)
マップ間冗長性(redundancy)は、マージされるべき複数のマップの分布の同時解析(joint analysis)に基づいて処理される
redundancy(s)=intraMapCr1(s)×intraMapCr2(s)
×Log[N/H(CS NQ Cr1,CS NQ Cr2)]/3Log(L)
Nは、関係するマップの部位の数を表す。
Claims (5)
- コンピュータによって実行される方法であって、
種々の視覚量から生成された複数の顕著性マップから一つの顕著性マップを得る方法であって、
・前記複数の顕著性マップを各視覚量の理論的な最大に基づいて規格化する段階と、
・各顕著性マップにおける主たる顕著性領域を抽出するマップ内競合段階と、
・前記マップ内競合段階から帰結する前記複数の顕著性マップを足し合わせた結果と、
前記諸マップ内競合とある部位が前記複数の顕著性マップ上に現れる確率との積の関数であるマップ間冗長性項と
の和に基づくマップ間競合段階と
を有することを特徴とする方法。 - 前記規格化段階において、
・前記複数の顕著性マップの諸部位が、視覚量の値の理論的な最大に対して規格化され、
・前記複数のマップが、所定の数のレベルを使って線形に量子化されることを特徴とする、請求項1記載の方法。 - 各顕著性マップにおける主たる顕著性領域を抽出する前記マップ内競合段階において、
・視覚量の値が大きいという意味で顕著な部位を含む第一のリストが定義され、
・前記顕著な部位の前記第一のリストが前記値の降順にスキャンされ、前記顕著な部位を中心とする所定の半径の円形領域内に存在する他の顕著な部位が禁止され、
・前記マップ内競合段階の結果として、次に大きい値の部位の値との比が所定の閾値より大きい非禁止部位を含む、前記諸顕著部位のリストが確立される請求項1記載の方法。 - 前記視覚量が色成分および無色成分に関する、請求項1ないし3のうちいずれか一項記載の方法。
- 複数の色成分に関する複数の顕著性マップがマージされ、次いで結果として得られる顕著性マップが無色成分に関する顕著性マップとマージされる、請求項4記載の方法。
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FR0552076 | 2005-07-06 | ||
FR0552076A FR2888375A1 (fr) | 2005-07-06 | 2005-07-06 | Procede d'obtention d'une carte de saillance a partir d'une pluralite de cartes de saillances etablies a partir de grandeurs visuelles differentes |
PCT/EP2006/063901 WO2007003651A1 (en) | 2005-07-06 | 2006-07-05 | Method of obtaining a saliency map from a plurality of saliency maps created from visual quantities |
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US20020154833A1 (en) * | 2001-03-08 | 2002-10-24 | Christof Koch | Computation of intrinsic perceptual saliency in visual environments, and applications |
GB2417808A (en) * | 2004-09-02 | 2006-03-08 | Hewlett Packard Development Co | Document creation system |
US8693780B2 (en) * | 2009-06-22 | 2014-04-08 | Technion Research & Development Foundation Limited | Automated collage formation from photographic images |
WO2011152893A1 (en) | 2010-02-10 | 2011-12-08 | California Institute Of Technology | Methods and systems for generating saliency models through linear and/or nonlinear integration |
JP6068329B2 (ja) | 2010-04-01 | 2017-01-25 | トムソン ライセンシングThomson Licensing | 立体表示用のサブタイトルを生成する方法及びシステム |
CN101853513B (zh) * | 2010-06-06 | 2012-02-29 | 华中科技大学 | 一种基于信息熵的时空显著性视觉注意方法 |
EP2582134A1 (en) * | 2011-10-12 | 2013-04-17 | Thomson Licensing | Saliency value determination of predictively encoded video streams |
US9626584B2 (en) * | 2014-10-09 | 2017-04-18 | Adobe Systems Incorporated | Image cropping suggestion using multiple saliency maps |
US11977319B2 (en) * | 2020-09-25 | 2024-05-07 | Qualcomm Incorporated | Saliency based capture or image processing |
CN115858792B (zh) * | 2023-02-20 | 2023-06-09 | 山东省计算中心(国家超级计算济南中心) | 基于图神经网络的招标项目名称短文本分类方法及系统 |
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EP1110381B1 (en) * | 1998-08-28 | 2003-07-16 | Sarnoff Corporation | Method and apparatus for electronically enhancing images |
US6282317B1 (en) | 1998-12-31 | 2001-08-28 | Eastman Kodak Company | Method for automatic determination of main subjects in photographic images |
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JP3848514B2 (ja) * | 2000-02-23 | 2006-11-22 | 富士通株式会社 | 顕著性算出装置及び方法 |
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FR2851677B1 (fr) * | 2003-02-20 | 2005-05-27 | France Telecom | Procede de selection d'un objet au sein d'une image source, dispositif, applications, et programme d'ordinateur correspondant. |
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FR2888375A1 (fr) | 2007-01-12 |
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CN101218605A (zh) | 2008-07-09 |
JP2009500736A (ja) | 2009-01-08 |
CN101218605B (zh) | 2012-10-31 |
EP1899915B1 (en) | 2017-05-03 |
US8326026B2 (en) | 2012-12-04 |
KR101240745B1 (ko) | 2013-03-07 |
US20090110269A1 (en) | 2009-04-30 |
WO2007003651A1 (en) | 2007-01-11 |
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