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<p class=3DMsoNormal><span lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>图像信息浩如烟=
海，针对图像有效的=
6816;索便是模式识别与ਿ=
4;能系统科学中的一个&=
#37325;要方向。目前一般=
212;用领域基于关键字的=
;检索技术使用较多。&#=
28982;而为图象加上关键é=
83;费时费力，而且经常=
不能准确的描述图象=
0340;内容。因而从图象ࠦ=
9;容中提取线索，直接&=
#23545;图象进行分析，抽=
462;特征，即基于内容的=
;图像检索应运而生。</=
span></p>
<p class=3DMsoNormal><span lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>图像特征包括形=
状、彩色、纹理、轮=
4275;、空间关系等。其ߑ=
3;彩色是图像的重要视&=
#35273;特征，在图像索引=
013;已得到广泛应用。对=
;彩色的描述有多种不&#=
21516;模型，其中</span><span
lang=3DEN-US>RGB</span><span style=3D'font-family:SimSun;mso-ascii-font-fam=
ily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>与</span><=
span
lang=3DEN-US>HSI</span><span style=3D'font-family:SimSun;mso-ascii-font-fam=
ily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>模型=
在图像索引中应用较=
2810;。图像通常可用直ਬ=
1;图加以描述，如全局&=
#30452;方图，局部直方图A=
292;累积直方图等。以之=
;为特征，再引入不同&#=
30340;相似性即可实现对Þ=
70;像的检索。</span></p>
<p class=3DMsoNormal><span lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal><b style=3D'mso-bidi-font-weight:normal'><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>课题关键问题及=
难点</span><span
lang=3DEN-US><o:p></o:p></span></b></p>
<p class=3DMsoNormal><span lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal><span style=3D'font-family:SimSun;mso-ascii-font-famil=
y:"Times New Roman";
mso-hansi-font-family:"Times New Roman"'>利用</span><span
lang=3DEN-US>VC++</span><span style=3D'font-family:SimSun;mso-ascii-font-fa=
mily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>构造=
该课题的试验平台系=
2479;，基本框架如下：</span=
></p>
<p class=3DMsoNormal align=3Dcenter style=3D'text-align:center'><span lang=
=3DEN-US><!--[if gte vml 1]><v:shapetype
id=3D"_x0000_t75" coordsize=3D"21600,21600" o:spt=3D"75" o:preferrelative=
=3D"t"
path=3D"m@4@5l@4@11@9@11@9@5xe" filled=3D"f" stroked=3D"f">
<v:stroke joinstyle=3D"miter"/>
<v:formulas>
<v:f eqn=3D"if lineDrawn pixelLineWidth 0"/>
<v:f eqn=3D"sum @0 1 0"/>
<v:f eqn=3D"sum 0 0 @1"/>
<v:f eqn=3D"prod @2 1 2"/>
<v:f eqn=3D"prod @3 21600 pixelWidth"/>
<v:f eqn=3D"prod @3 21600 pixelHeight"/>
<v:f eqn=3D"sum @0 0 1"/>
<v:f eqn=3D"prod @6 1 2"/>
<v:f eqn=3D"prod @7 21600 pixelWidth"/>
<v:f eqn=3D"sum @8 21600 0"/>
<v:f eqn=3D"prod @7 21600 pixelHeight"/>
<v:f eqn=3D"sum @10 21600 0"/>
</v:formulas>
<v:path o:extrusionok=3D"f" gradientshapeok=3D"t" o:connecttype=3D"rect"/>
<o:lock v:ext=3D"edit" aspectratio=3D"t"/>
</v:shapetype><v:shape id=3D"_x0000_i1025" type=3D"#_x0000_t75" style=3D'wi=
dth:398.25pt;
height:156pt'>
<v:imagedata src=3D"file6058.files/image001.jpg" o:title=3D"1"/>
</v:shape><![endif]--><![if !vml]><img width=3D531 height=3D208
src=3D"file6058.files/image001.jpg" v:shapes=3D"_x0000_i1025"><![endif]></s=
pan></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>根据计算机信息=
系统的</span><span
lang=3DEN-US>MVC</span><span style=3D'font-family:SimSun;mso-ascii-font-fam=
ily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>模型=
原理，设计实体数据=
4211;，控制检索算法，ஷ=
2;户界面三个部分。显&=
#28982;检索算法实现是核=
515;。</span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>对于目标图像和=
检索图像进行颜色空=
8388;转换、亮度图像的๟=
3;缘提取和二值分割、&=
#25552;取目标区域的颜色=
305;征。颜色内容包含两=
;个一般的概念，一个&#=
23545;应于全局颜色分布ʌ=
92;一个对应于局部颜色=
信息。按照全局颜色=
0998;布来索引图像可以๩=
0;过计算每种颜色的像&=
#32032;的个数并构造颜色=
784;度直方图来实现，这=
;对检索具有相似的总&#=
20307;颜色内容的图像是Ç=
68;个很好的途径。局部=
颜色信息是指局部相=
0284;的颜色区域，它考൮=
5;了颜色的分类与一些&=
#21021;级的几何特征。比=
914;</span><span
lang=3DEN-US>Smith</span><span style=3D'font-family:SimSun;mso-ascii-font-f=
amily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>等提=
出了颜色集合</span><span
lang=3DEN-US>(color set)</span><span style=3D'font-family:SimSun;mso-ascii-=
font-family:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>方法=
来抽取空间局部颜色=
0449;息并提供颜色区域௚=
0;有效索引。</span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>最后，通过平台=
系统试验结果比较出=
0840;局直方图方法，局๽=
6;直方图方法，累积直&=
#26041;图方的性能。</span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal><b style=3D'mso-bidi-font-weight:normal'><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>调研报告（文献=
综述）</span><span
lang=3DEN-US><o:p></o:p></span></b></p>
<p class=3DMsoNormal><b style=3D'mso-bidi-font-weight:normal'><span lang=3D=
EN-US><o:p> </o:p></span></b></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
style=3D'font-family:SimSun;mso-ascii-font-family:"Times New Roman";mso-han=
si-font-family:
"Times New Roman"'>图像检索自</span><span
lang=3DEN-US>70 </span><span style=3D'font-family:SimSun;mso-ascii-font-fam=
ily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>年代=
来一直是个非常活跃=
0340;研究方向。早期的ࢳ=
0;像检索是基于图像关&=
#38190;字的检索，该方法&=
656;要人工对每幅图像按=
;其内容进行标注，然&#=
21518;将标注信息存到文Ĉ=
12;数据库中用于后来的=
检索。显然，随着图=
0687;的增多，人工标注༣=
0;常困难，而且，每个&=
#20154;对图像内容的理解=
981;同会造成标注的主观=
;性过强，不利于用户&#=
26816;索。</span></p>
<p class=3DMsoNormal><span lang=3DEN-US><o:p> </o:p></span></p>
<p class=3DMsoNormal style=3D'text-indent:21.0pt;mso-char-indent-count:2.0'=
><span
lang=3DEN-US>90</span><span style=3D'font-family:SimSun;mso-ascii-font-fami=
ly:"Times New Roman";
mso-hansi-font-family:"Times New Roman"'>年代以后&#=
65292;图像检索研究重点ą=
59;基于图像内容的检索=
</span><span
lang=3DEN-US>(CBIR)</span><span style=3D'font-family:SimSun;mso-ascii-font-=
family:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>，</span><=
span
lang=3DEN-US>CBIR</span><span style=3D'font-family:SimSun;mso-ascii-font-fa=
mily:
"Times New Roman";mso-hansi-font-family:"Times New Roman"'>指的=
是在数据库中找出满=
6275;某一特定的视觉特ঌ=
9;描述的图像的过程。&=
#23427;的基本思想是通过=
998;析图像的视觉特征和=
;上下文联系来进行检&#=
32034;。其中，图像内容ą=
59;通过图像的特征来反=
映的，可以将图像的=
9305;征分为两大类，即ॵ=
3;层物理特征</span><span
lang=3DEN-US>(</span><span style=3D'font-family:SimSun;mso-ascii-font-famil=
y:"Times New Roman";
mso-hansi-font-family:"Times New Roman"'>如颜色、&#=
32441;理、形状、轮廓、Þ=
70;像内容的空间、时间=
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