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		<title>Self organizing natural scene image retrieval - Versionsgeschichte</title>
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		<title>Gubachelier: Die Seite wurde neu angelegt: „ == Referenz ==  José Félix Serrano-Talamantes, Carlos Avilés-Cruz, Juan Villegas-Cortez, Juan H. Sossa-Azuela: Self organizing natural scene image retrie…“</title>
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		<summary type="html">&lt;p&gt;Die Seite wurde neu angelegt: „ == Referenz ==  José Félix Serrano-Talamantes, Carlos Avilés-Cruz, Juan Villegas-Cortez, Juan H. Sossa-Azuela: Self organizing natural scene image retrie…“&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Neue Seite&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== Referenz == &lt;br /&gt;
José Félix Serrano-Talamantes, Carlos Avilés-Cruz, Juan Villegas-Cortez, Juan H. Sossa-Azuela: [[Self organizing natural scene image retrieval]]. Expert Systems with Applications, vol. 40, no. 7, pp. 2398-2409, 2013 &lt;br /&gt;
&lt;br /&gt;
== DOI ==&lt;br /&gt;
http://dx.doi.org/10.1016/j.eswa.2012.10.064&lt;br /&gt;
&lt;br /&gt;
== Abstract ==&lt;br /&gt;
In this work we describe a new statistically-based methodology to organize and retrieve images of natural scenes by combining feature extraction, automatic clustering, automatic indexing and classification techniques. Our proposal belongs to the content-based image retrieval (CBIR) category. Our goal is to retrieve images from an image database by their content. The methodology combines randomly extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (from the co-occurrence matrix) of a sub-image extracted from the three color channels (HSI). A K-means algorithm and a 1-NN classifier are used to build an indexed database. Three databases of images of natural scenes are used during the training and testing processes. One of the advantages of our proposal is that the images are not labeled manually for their retrieval. The performance of our framework is shown through several experimental results, including a comparison with several classifiers and comparison with related works, achieving up to 100% good recognition. Additionally, our proposal includes scene retrieval.&lt;br /&gt;
&lt;br /&gt;
== Extended Abstract ==&lt;br /&gt;
&lt;br /&gt;
== Bibtex == &lt;br /&gt;
 @article{SerranoTalamantes20132398,&lt;br /&gt;
 title = &amp;quot;Self organizing natural scene image retrieval &amp;quot;,&lt;br /&gt;
 journal = &amp;quot;Expert Systems with Applications &amp;quot;,&lt;br /&gt;
 volume = &amp;quot;40&amp;quot;,&lt;br /&gt;
 number = &amp;quot;7&amp;quot;,&lt;br /&gt;
 pages = &amp;quot;2398 - 2409&amp;quot;,&lt;br /&gt;
 year = &amp;quot;2013&amp;quot;,&lt;br /&gt;
 note = &amp;quot;&amp;quot;,&lt;br /&gt;
 issn = &amp;quot;0957-4174&amp;quot;,&lt;br /&gt;
 doi = &amp;quot;http://dx.doi.org/10.1016/j.eswa.2012.10.064&amp;quot;,&lt;br /&gt;
 url = &amp;quot;http://www.sciencedirect.com/science/article/pii/S0957417412011888 http://de.evo-art.org/index.php?title=Self_organizing_natural_scene_image_retrieval&amp;quot;,&lt;br /&gt;
 author = &amp;quot;José Félix Serrano-Talamantes and Carlos Avilés-Cruz and Juan Villegas-Cortez and Juan H. Sossa-Azuela&amp;quot;,&lt;br /&gt;
 keywords = &amp;quot;Image analysis&amp;quot;,&lt;br /&gt;
 keywords = &amp;quot;Image processing&amp;quot;,&lt;br /&gt;
 keywords = &amp;quot;Content-based image retrieval (CBIR)&amp;quot;,&lt;br /&gt;
 keywords = &amp;quot;Feature extraction&amp;quot;,&lt;br /&gt;
 keywords = &amp;quot;Indexed database &amp;quot;&lt;br /&gt;
 }&lt;br /&gt;
&lt;br /&gt;
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== Links == &lt;br /&gt;
&lt;br /&gt;
=== Full Text === &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
[[internal file]] &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== Sonstige Links ===&lt;/div&gt;</summary>
		<author><name>Gubachelier</name></author>	</entry>

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