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    <title>Magnolia - Issue tracker</title>
    <link>https://jira.magnolia-cms.com</link>
    <description>This file is an XML representation of an issue</description>
    <language>en-uk</language>    <build-info>
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        <build-number>940002</build-number>
        <build-date>19-01-2023</build-date>
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<item>
            <title>[MGNLPER-17] Recognize typical marketing images reliably</title>
                <link>https://jira.magnolia-cms.com/browse/MGNLPER-17</link>
                <project id="14882" key="MGNLPER">Periscope</project>
                    <description>&lt;p&gt;User story:&lt;/p&gt;
&lt;blockquote&gt;&lt;p&gt;As a digital marketer, I want to find the right image asset quickly. Image recognition helps me as long as it recognizes objects in typical marketing images and tags the assets accordingly. I expect common marketing subjects (&quot;car&quot;, &quot;dog&quot;, &quot;person&quot;, &quot;office&quot;, &quot;beach&quot; etc.) to be always recognized and tagged. I don&apos;t expect specialized subjects (&quot;trilobite&quot;, &quot;Cockerspaniel&quot;, &quot;Daniel Radcliffe&quot;) to be always recognized but I am positively surprised if they are.&lt;/p&gt;&lt;/blockquote&gt;
&lt;p&gt;Internal story:&lt;/p&gt;
&lt;blockquote&gt;&lt;p&gt;As a Magnolia Product Manager, I want local image recognition to be sufficiently complete and accurate so that it impresses evaluators. I want typical marketing subjects to be always recognized and tagged. For specialized images I expect the client to use an integrated image recognition service.&lt;/p&gt;&lt;/blockquote&gt;
&lt;p&gt;Business benefit / value: Reliable and accurate local image recognition makes a great first impression. It convinces evaluators.&lt;/p&gt;

&lt;p&gt;Background: Local image recognition is currently limited to&#160;&lt;a href=&quot;http://image-net.org/challenges/LSVRC/2014/browse-synsets&quot; class=&quot;external-link&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener&quot;&gt;ImageNet 1000 synsets&lt;/a&gt; (synonym sets). This collection of labels does not represent typical marketing imagery. It is heavily biased towards animals (&quot;African elephant&quot;, &quot;hyena&quot;, &quot;weasel&quot;) while common marketing subjects like &quot;computer&quot;, &quot;person&quot; and &quot;shoe&quot; are missing. This means that a neural network pre-trained on Imagenet 1000 classifications does not recognize common marketing subjects.&lt;/p&gt;

&lt;p&gt;Acceptance criteria:&lt;/p&gt;
&lt;ul&gt;
	&lt;li&gt;Common marketing image subjects (&quot;man&quot;, &quot;woman&quot;, &quot;child&quot;, &quot;computer&quot;, &quot;office&quot;, &quot;beach&quot;) are recognized 90% of the time.&lt;/li&gt;
	&lt;li&gt;At least 3 tags are applied to common marketing subjects. If an easily recognizable image gets no tags it gives a poor first impression.&lt;/li&gt;
	&lt;li&gt;Tags are nouns. Exclude verbs, adjectives and adverbs.&lt;/li&gt;
&lt;/ul&gt;


&lt;p&gt;Implementation proposal (optional, up to PD to decide):&lt;/p&gt;
&lt;ul&gt;
	&lt;li&gt;For a set of labels that better represent subjects in typical marketing images, use&#160;&lt;a href=&quot;https://wordnet.princeton.edu/download/standoff-files&quot; class=&quot;external-link&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener&quot;&gt;Core Wordnet 5000.&lt;/a&gt;&#160;It is a list of 5000 core word senses in WordNet. The list contains most frequently used English language words. Download: &lt;a href=&quot;http://wordnetcode.princeton.edu/standoff-files/core-wordnet.txt&quot; class=&quot;external-link&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener&quot;&gt;http://wordnetcode.princeton.edu/standoff-files/core-wordnet.txt&lt;/a&gt;&lt;/li&gt;
	&lt;li&gt;Use nouns only. Exclude verbs, adjectives and adverbs.&lt;/li&gt;
	&lt;li&gt;Generate a new labels &lt;a href=&quot;https://git.magnolia-cms.com/projects/ENTERPRISE/repos/image-recognition/browse/local-image-recognition/src/main/resources/info.magnolia.ai.image.local/imageNetLabels.json&quot; class=&quot;external-link&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener&quot;&gt;JSON file&lt;/a&gt; and re-train the neural network on ImageNet images.&lt;/li&gt;
&lt;/ul&gt;


&lt;p&gt;Attachments:&lt;/p&gt;
&lt;ul&gt;
	&lt;li&gt;marketing-images.zip &#8211; set of common marketing subjects for testing.&lt;/li&gt;
&lt;/ul&gt;
</description>
                <environment></environment>
        <key id="65177">MGNLPER-17</key>
            <summary>Recognize typical marketing images reliably</summary>
                <type id="3" iconUrl="https://jira.magnolia-cms.com/secure/viewavatar?size=xsmall&amp;avatarId=10898&amp;avatarType=issuetype">Task</type>
                                            <priority id="6" iconUrl="https://jira.magnolia-cms.com/images/icons/priorities/neutral.gif">Neutral</priority>
                        <status id="6" iconUrl="https://jira.magnolia-cms.com/images/icons/statuses/closed.png" description="The issue is considered finished, the resolution is correct. Issues which are not closed can be reopened.">Closed</status>
                    <statusCategory id="3" key="done" colorName="success"/>
                                    <resolution id="11">Done</resolution>
                                        <assignee username="creichenbach">Cedric Reichenbach</assignee>
                                    <reporter username="ahietala">Antti Hietala</reporter>
                        <labels>
                    </labels>
                <created>Mon, 26 Mar 2018 11:13:43 +0200</created>
                <updated>Tue, 7 May 2019 09:06:08 +0200</updated>
                            <resolved>Fri, 26 Oct 2018 10:42:13 +0200</resolved>
                                                    <fixVersion>1.0</fixVersion>
                                        <due></due>
                            <votes>0</votes>
                                    <watches>3</watches>
                                                                                                                <comments>
                            <comment id="170997" author="ahietala" created="Fri, 12 Oct 2018 08:41:37 +0200"  >&lt;p&gt;Timebox research to 5 SP&lt;/p&gt;</comment>
                            <comment id="172068" author="creichenbach" created="Fri, 26 Oct 2018 08:58:46 +0200"  >&lt;p&gt;Outcome after timeboxed effort: There are no useful-enough pretrained networks available.&lt;/p&gt;

&lt;p&gt;The most useful one is probably TinyYOLO, which is pretty accurate and detects classes like &quot;person&quot; or &quot;car&quot;, but still only supports 20 different classes. Also, it also detects (boundling-box) object locations, which we don&apos;t need here and just causes additional computation cost. Here a quick draft integration: &lt;a href=&quot;https://git.magnolia-cms.com/users/creichenbach/repos/image-recognition/commits?until=refs%2Fheads%2FMGNLPER-17-more-relevant-tags&amp;amp;merges=include&quot; class=&quot;external-link&quot; target=&quot;_blank&quot; rel=&quot;nofollow noopener&quot;&gt;https://git.magnolia-cms.com/users/creichenbach/repos/image-recognition/commits?until=refs%2Fheads%2FMGNLPER-17-more-relevant-tags&amp;amp;merges=include&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;See the linked follow-up issue for more infos about how to potentially proceed with a customly trained network.&lt;/p&gt;</comment>
                    </comments>
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                                        <issuelink>
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                            <attachment id="47845" name="marketing-images.zip" size="3899687" author="ahietala" created="Mon, 10 Sep 2018 15:09:07 +0200"/>
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