A suitable feature vector for images
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I have a set of images of various products from different websites. I want to cluster the images based on the product shown in the image. How can I generate a suitable feature vector for an image for this purpose?? I just need to know how to generate a feature vector given an image. I tried NetVLAD, but it is very slow. I would like something that is fast and gives high accuracy for clustering in the scenario I have described. Please help me.
clustering feature-extraction
asked Jan 8 at 10:02
user65516user65516
61
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bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have a set of images of various products from different websites. I want to cluster the images based on the product shown in the image. How can I generate a suitable feature vector for an image for this purpose?? I just need to know how to generate a feature vector given an image. I tried NetVLAD, but it is very slow. I would like something that is fast and gives high accuracy for clustering in the scenario I have described. Please help me.
clustering feature-extraction
asked Jan 8 at 10:02
user65516user65516
61
$endgroup$
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations...
$endgroup$
– Mark.F
Jan 8 at 11:42
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There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most).
$endgroup$
– user65516
Jan 9 at 15:06
$begingroup$
Can someone help me with this??
$endgroup$
– user65516
Jan 10 at 6:25
add a comment |
$begingroup$
I have a set of images of various products from different websites. I want to cluster the images based on the product shown in the image. How can I generate a suitable feature vector for an image for this purpose?? I just need to know how to generate a feature vector given an image. I tried NetVLAD, but it is very slow. I would like something that is fast and gives high accuracy for clustering in the scenario I have described. Please help me.
clustering feature-extraction
asked Jan 8 at 10:02
user65516user65516
61
$endgroup$
I have a set of images of various products from different websites. I want to cluster the images based on the product shown in the image. How can I generate a suitable feature vector for an image for this purpose?? I just need to know how to generate a feature vector given an image. I tried NetVLAD, but it is very slow. I would like something that is fast and gives high accuracy for clustering in the scenario I have described. Please help me.
clustering feature-extraction
clustering feature-extraction
asked Jan 8 at 10:02
user65516user65516
61
asked Jan 8 at 10:02
user65516user65516
61
asked Jan 8 at 10:02
user65516user65516
61
asked Jan 8 at 10:02
user65516user65516
61
asked Jan 8 at 10:02
user65516user65516
61
61
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations...
$endgroup$
– Mark.F
Jan 8 at 11:42
$begingroup$
There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most).
$endgroup$
– user65516
Jan 9 at 15:06
$begingroup$
Can someone help me with this??
$endgroup$
– user65516
Jan 10 at 6:25
add a comment |
$begingroup$
You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations...
$endgroup$
– Mark.F
Jan 8 at 11:42
$begingroup$
There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most).
$endgroup$
– user65516
Jan 9 at 15:06
$begingroup$
Can someone help me with this??
$endgroup$
– user65516
Jan 10 at 6:25
$begingroup$
You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations...
$endgroup$
– Mark.F
Jan 8 at 11:42
$begingroup$
You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations...
$endgroup$
– Mark.F
Jan 8 at 11:42
$begingroup$
There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most).
$endgroup$
– user65516
Jan 9 at 15:06
$begingroup$
There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most).
$endgroup$
– user65516
Jan 9 at 15:06
$begingroup$
Can someone help me with this??
$endgroup$
– user65516
Jan 10 at 6:25
$begingroup$
Can someone help me with this??
$endgroup$
– user65516
Jan 10 at 6:25
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Feature extraction is basically reducing the amount of resources required to describe a large set of data. Analysis with a large number of variables (like images) generally requires a big amounts of memory and computation power.
You can start from the simplest and slowly work your way up (in computational requirements) until you reach your desired accuracy.
Start from:
ORB (oriented FAST, rotated robust independent features) -> SURF (speed up robust feature) -> SIFT (scale invariant feature transform) -> OverFeat (Convolutional Network) -> vgg16 ... (There are many more, any neural network model can be used for this)
answered Jan 10 at 15:37
Mark.FMark.F
766218
$endgroup$
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
add a comment |
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1 Answer
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1 Answer
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$begingroup$
Feature extraction is basically reducing the amount of resources required to describe a large set of data. Analysis with a large number of variables (like images) generally requires a big amounts of memory and computation power.
You can start from the simplest and slowly work your way up (in computational requirements) until you reach your desired accuracy.
Start from:
ORB (oriented FAST, rotated robust independent features) -> SURF (speed up robust feature) -> SIFT (scale invariant feature transform) -> OverFeat (Convolutional Network) -> vgg16 ... (There are many more, any neural network model can be used for this)
answered Jan 10 at 15:37
Mark.FMark.F
766218
$endgroup$
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
add a comment |
$begingroup$
Feature extraction is basically reducing the amount of resources required to describe a large set of data. Analysis with a large number of variables (like images) generally requires a big amounts of memory and computation power.
You can start from the simplest and slowly work your way up (in computational requirements) until you reach your desired accuracy.
Start from:
ORB (oriented FAST, rotated robust independent features) -> SURF (speed up robust feature) -> SIFT (scale invariant feature transform) -> OverFeat (Convolutional Network) -> vgg16 ... (There are many more, any neural network model can be used for this)
answered Jan 10 at 15:37
Mark.FMark.F
766218
$endgroup$
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
add a comment |
$begingroup$
Feature extraction is basically reducing the amount of resources required to describe a large set of data. Analysis with a large number of variables (like images) generally requires a big amounts of memory and computation power.
You can start from the simplest and slowly work your way up (in computational requirements) until you reach your desired accuracy.
Start from:
ORB (oriented FAST, rotated robust independent features) -> SURF (speed up robust feature) -> SIFT (scale invariant feature transform) -> OverFeat (Convolutional Network) -> vgg16 ... (There are many more, any neural network model can be used for this)
answered Jan 10 at 15:37
Mark.FMark.F
766218
$endgroup$
Feature extraction is basically reducing the amount of resources required to describe a large set of data. Analysis with a large number of variables (like images) generally requires a big amounts of memory and computation power.
You can start from the simplest and slowly work your way up (in computational requirements) until you reach your desired accuracy.
Start from:
ORB (oriented FAST, rotated robust independent features) -> SURF (speed up robust feature) -> SIFT (scale invariant feature transform) -> OverFeat (Convolutional Network) -> vgg16 ... (There are many more, any neural network model can be used for this)
answered Jan 10 at 15:37
Mark.FMark.F
766218
answered Jan 10 at 15:37
Mark.FMark.F
766218
answered Jan 10 at 15:37
Mark.FMark.F
766218
answered Jan 10 at 15:37
Mark.FMark.F
766218
766218
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
add a comment |
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
$begingroup$
I would like to try out ORB. But, I am facing a difficulty. Given two different images, how do I find the distance between them using ORB features??
$endgroup$
– user65516
Jan 16 at 15:58
add a comment |
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$begingroup$
You should add more info about your dataset (size, number of samples, number of desired clusters). You should also explain what do you mean by slow/fast and high accuracy. These things mainly depend on your computational limitations...
$endgroup$
– Mark.F
Jan 8 at 11:42
$begingroup$
There are 282,082 images with a total size of almost 60 GB. I don't have a desired number of clusters. I want an accuracy of better than 80%. I want it to be fast enough to run on a workstation in a couple of days (at most).
$endgroup$
– user65516
Jan 9 at 15:06
$begingroup$
Can someone help me with this??
$endgroup$
– user65516
Jan 10 at 6:25