Classification of jumbled images
$begingroup$
I want to be able to create a model that would be able to classify an image that has been split into 9 parts and jumbled around.
I did see a paper on it but it is quite old (7-8 years old). Could anyone point me towards any resources? Is building a CNN the best approach?
Any help is appreciated.
image-classification computer-vision
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
$endgroup$
add a comment |
$begingroup$
I want to be able to create a model that would be able to classify an image that has been split into 9 parts and jumbled around.
I did see a paper on it but it is quite old (7-8 years old). Could anyone point me towards any resources? Is building a CNN the best approach?
Any help is appreciated.
image-classification computer-vision
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
$endgroup$
$begingroup$
You want to classify it based on the content as if it had not been jumbled, like a typical image recognition problem?
$endgroup$
– Emre
Jan 16 '18 at 23:21
add a comment |
$begingroup$
I want to be able to create a model that would be able to classify an image that has been split into 9 parts and jumbled around.
I did see a paper on it but it is quite old (7-8 years old). Could anyone point me towards any resources? Is building a CNN the best approach?
Any help is appreciated.
image-classification computer-vision
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
$endgroup$
I want to be able to create a model that would be able to classify an image that has been split into 9 parts and jumbled around.
I did see a paper on it but it is quite old (7-8 years old). Could anyone point me towards any resources? Is building a CNN the best approach?
Any help is appreciated.
image-classification computer-vision
image-classification computer-vision
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
edited Jan 16 '18 at 15:45
Dawny33♦
5,47683188
5,47683188
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
asked Jan 16 '18 at 15:38
NobleSiksNobleSiks
112
112
$begingroup$
You want to classify it based on the content as if it had not been jumbled, like a typical image recognition problem?
$endgroup$
– Emre
Jan 16 '18 at 23:21
add a comment |
$begingroup$
You want to classify it based on the content as if it had not been jumbled, like a typical image recognition problem?
$endgroup$
– Emre
Jan 16 '18 at 23:21
$begingroup$
You want to classify it based on the content as if it had not been jumbled, like a typical image recognition problem?
$endgroup$
– Emre
Jan 16 '18 at 23:21
$begingroup$
You want to classify it based on the content as if it had not been jumbled, like a typical image recognition problem?
$endgroup$
– Emre
Jan 16 '18 at 23:21
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Some options:
CNNs are indeed state-of-the-art in computer vision for image recognition, categorization, and classification. Simply taking the jumbled images and learning a mapping from them to the label via a CNN is likely to be the most straightforward and likely to work approach.
One thing missing from the above idea for problems where the label one is trying to learn relies on some global structurally coherent aspects, which are destroyed during the scrambling. In this case, one can either try to learn to reconstruct the images (see below) or take every image and try several random rearrangements as input (per permuted image), and take, say, the prediction the network is most confident in.
Separately, if you want to reconstruct the jumbled images (i.e., solve the scrambled puzzle), there are some recent papers looking at how to do exactly that. E.g.,
DeepPermNet: Visual Permutation Learning (2017)
Learning Latent Permutations with Gumbel-Sinkhorn Networks (2018)
answered 7 mins ago
user3658307user3658307
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f26698%2fclassification-of-jumbled-images%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Some options:
CNNs are indeed state-of-the-art in computer vision for image recognition, categorization, and classification. Simply taking the jumbled images and learning a mapping from them to the label via a CNN is likely to be the most straightforward and likely to work approach.
One thing missing from the above idea for problems where the label one is trying to learn relies on some global structurally coherent aspects, which are destroyed during the scrambling. In this case, one can either try to learn to reconstruct the images (see below) or take every image and try several random rearrangements as input (per permuted image), and take, say, the prediction the network is most confident in.
Separately, if you want to reconstruct the jumbled images (i.e., solve the scrambled puzzle), there are some recent papers looking at how to do exactly that. E.g.,
DeepPermNet: Visual Permutation Learning (2017)
Learning Latent Permutations with Gumbel-Sinkhorn Networks (2018)
answered 7 mins ago
user3658307user3658307
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Some options:
CNNs are indeed state-of-the-art in computer vision for image recognition, categorization, and classification. Simply taking the jumbled images and learning a mapping from them to the label via a CNN is likely to be the most straightforward and likely to work approach.
One thing missing from the above idea for problems where the label one is trying to learn relies on some global structurally coherent aspects, which are destroyed during the scrambling. In this case, one can either try to learn to reconstruct the images (see below) or take every image and try several random rearrangements as input (per permuted image), and take, say, the prediction the network is most confident in.
Separately, if you want to reconstruct the jumbled images (i.e., solve the scrambled puzzle), there are some recent papers looking at how to do exactly that. E.g.,
DeepPermNet: Visual Permutation Learning (2017)
Learning Latent Permutations with Gumbel-Sinkhorn Networks (2018)
answered 7 mins ago
user3658307user3658307
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Some options:
CNNs are indeed state-of-the-art in computer vision for image recognition, categorization, and classification. Simply taking the jumbled images and learning a mapping from them to the label via a CNN is likely to be the most straightforward and likely to work approach.
One thing missing from the above idea for problems where the label one is trying to learn relies on some global structurally coherent aspects, which are destroyed during the scrambling. In this case, one can either try to learn to reconstruct the images (see below) or take every image and try several random rearrangements as input (per permuted image), and take, say, the prediction the network is most confident in.
Separately, if you want to reconstruct the jumbled images (i.e., solve the scrambled puzzle), there are some recent papers looking at how to do exactly that. E.g.,
DeepPermNet: Visual Permutation Learning (2017)
Learning Latent Permutations with Gumbel-Sinkhorn Networks (2018)
answered 7 mins ago
user3658307user3658307
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Some options:
CNNs are indeed state-of-the-art in computer vision for image recognition, categorization, and classification. Simply taking the jumbled images and learning a mapping from them to the label via a CNN is likely to be the most straightforward and likely to work approach.
One thing missing from the above idea for problems where the label one is trying to learn relies on some global structurally coherent aspects, which are destroyed during the scrambling. In this case, one can either try to learn to reconstruct the images (see below) or take every image and try several random rearrangements as input (per permuted image), and take, say, the prediction the network is most confident in.
Separately, if you want to reconstruct the jumbled images (i.e., solve the scrambled puzzle), there are some recent papers looking at how to do exactly that. E.g.,
DeepPermNet: Visual Permutation Learning (2017)
Learning Latent Permutations with Gumbel-Sinkhorn Networks (2018)
answered 7 mins ago
user3658307user3658307
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 7 mins ago
user3658307user3658307
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 7 mins ago
user3658307user3658307
1012
answered 7 mins ago
user3658307user3658307
1012
1012
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user3658307 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f26698%2fclassification-of-jumbled-images%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
You want to classify it based on the content as if it had not been jumbled, like a typical image recognition problem?
$endgroup$
– Emre
Jan 16 '18 at 23:21