I am very new to machine learning and have been implementing ML algorithms on the datasets.
But how do I go about classifying images using the Ml algorithms?
How do I feed the images to the learning models in the form of numpy arrays?
Can anyone brief me about the steps involved? I have been reading about feature extraction but I am not able to figure out how to do that.
Image classification is not much different, at its core, from any other sort of classification.
Your data are images, right? Well, we need to create some variables ("features") from those images in order to get a sense of what's in the images. Computers can understand matrices, not just straight-up images like humans do (although there are arguments that what humans are doing when they see images is deconstructing images into patterns of pixels, but let's keep it simple). Using OpenCV is a great way to turn image pixels into matrices.
Each matrix (i.e. each image) will have a corresponding tag or classification (e.g. "dog" or "cat"). You feed those matrices through your algorithm in order to classify each image.
That will get you started. There's so much that goes into machine learning related to images, but at its core, the problem is the same as elsewhere: take a matrix/set of data and use an algorithm to find patterns in the data and a function that maps the input to the output label. You might be served well by reading an intro to machine learning book or taking a course.
Related
As an example I have two pictures with a particular type of clothing of a certain brand.
I can download a lot of different images of this same piece, and color, of clothing
I want to create a model which can recognize the item based on a picture.
I tried to do it using this example:
https://www.tensorflow.org/tutorials/keras/classification.
This can recognize the type of clothing (eg shirt or shoe or trousers, etc) But not a specific item and color.
My goal is to have a model that can tell me that the person on my first picture is wearing the item of my second picture.
As mentioned I can upload a few variations of this same item to train my model, if that would be the best approach.
I also tried to use https://pillow.readthedocs.io
This can do something with color recognition but does not solve my initial goal.
i don't think that CNN can help you in your problemes, take a look at the SIFT Technique see this for more détails.it is used for image matching and i think it's better in your cas. if your not looking to get in to much detailes the opencv is a python (and c++ i think) library that has image matching function that are easy to use more détails .
As mentionned by #nadji mansouri, I would use SIFT technique as it suits your need. But I want just to correct something, CNN is also a thing in this case. This being said, I wouldn't tackle the problem as a classification problem, but rather using Distance Metric Learning, i.e, training a model to generate embeddings that are similar in the space when the inputs are similar, and distant otherwise. But to do this you need a large representative dataset.
In short, I suggest starting with SIFT, using OpenCV, or open source implementations on GitHub, playing around with the parameters and see what fits your case best, and then see if it's really necessary to switch to a neural network, and in this case tackling the problem as a metric learning task, maybe with something like siamese networks.
Some definitions:
Metric learning is an approach based directly on a distance metric that aims to establish similarity or dissimilarity between data (images in your case). Deep Metric Learning on the other hand uses Neural Networks to automatically learn discriminative features from the data and then compute the metric. source.
The Scale-Invariant Feature Transform (SIFT) is a method used in computer vision to detect and describe local features in images. The algorithm is invariant to image scale and rotation, and robust to changes in illumination and affine distortion. SIFT features are represented by local image gradients, which are calculated at various scales and orientations, and are used to identify keypoints in an image. These keypoints and their associated descriptor vectors can then be used for tasks such as image matching, object recognition, and structure from motion. source, with modification.
I have a machine learning project. For data, I have 200k photos and each photo represent one class, so I will have 200k classes (e.g. I could have 200k images of rare bird, in which there is only one image available for each of them. Now, the problem is which method should I use for image detection with so little data?
Thank you.
I have a folder with hundres/thousands of images, some of them look alike. I would like to create clusters separating those images (those which look alike in the same cluster).
I can't determine the number of clusters that will be needed, it depends on the images.
Does anyone have an idea on how to do this using Python, OpenCV and which algorithm to use?
I've made some research and found that AffinityPropagation or DBSCAN can be useful for me but I don't know where to start (how to encode my images, what should I pass to those algorithms etc...)
Unfortunately it is not that simple with images, since naively clustering would result in clusters of images with the same colors, not the same "content". You can use a neural network as a feature extractor for the images, I see two options:
Use a pre-trained network and get the features from an intermediate layer
Train an autoencoder on your dataset, and use the latent features
Option 1 is cheaper since you can easily find pre-trained models, option 2 is much more computationally expensive but should work better, especially if there is no pre-trained model on your domain.
This tutorial (randomly found on the internet) seems to be a good introduction to method 2.
I'd like to implement something like the title, but I wonder if it's technically possible.
I know that it is possible to recognize pictures with CNN,
but I don't know if can be automatically covered nipple area.
If have library information about any related information,
I would like to get some advice.
CNNs are able to detect whatever you train them for, to varying degree of accuracy. What you would need are a lot of training samples (ie. samples of ground truths with the original image, and the labeled image) with which to train your models, and then some new data which you can test the accuracy of your model on. The point is, CNNs are not biased to innately learn a task, you have to tell them what to learn!
I can recommend the machine learning library Keras (https://keras.io/) if you plan to do some machine learning using CNNs, as it's pretty simple and somewhat beginner-friendly. Take some of the tutorials for CNNs, which are quite good.
Essentially, you have what I can only assume is a pretty niche problem. The main issue will come down to how much data you have to train your model. CNNs need a lot of training data, especially for a problem like this which isn't simple. A way which would make this simpler would be to have a model which detects the ahem area of interest and denotes it as such on a per-pixel basis. Then a simple mask could be applied to the source image to censor it. This relates to image segmentation, and there are many academic papers on the topic.
So here is my question:
I want to make my very own dataset using a motion capture camera system to get the ground truth poses and one RGB camera to get images, and then using this as input to my network, train/test a convNet.
I have looked around at other datasets for tensorflow, caffe and Matlab. I have viewed the MNIST, Cats/Dogs, Iris, LSP, HumanEva, HumanEva3.6, FLIC, etc. datasets and have viewed and tried to understand their data as best as I can. I have viewed online people trying to make their own datasets. The one thing is usually when you use their datasets as an example, you download a .txt file that already contains the labels.
If anyone could please explain to me how to use the image data with the labels to feed it into my network, it would be a tremendous help. I have made code before using tensorflow to input a .txt file into the network and get the correct predicted output. But, my brain is missing something to understand how to input an image with a label. How to I create that dataset?
Your input images and your labels are two separate variables. You will be writing separate bits of code to import them. The videos typically need to be converted to JPG files (it's a royal pain to read video files directly, mostly because you can't randomly skip around the video easily).
Probably the easiest way to structure you data is via a CSV that contains filename, poseinfoA, poseinfoB, etc. And the filename refers to the JPG image on disk.
To get started on the basics, I suggest looking at the Aymericdamen tutorial examples, I haven't found tutorials anywhere that were as clear and concise.
https://github.com/aymericdamien/TensorFlow-Examples
Those examples don't go into detail on the data input pipeline though. To set up a good data input pipeline in tensorflow I suggest you use the new (as of TF 1.4) Dataset object. It will force you into a good data input pipline workflow, and it's the way all data input is going in tensorflow, so it's worth learning. It's also easy to test and debug when you write it this way. Here's the guide you want to follow.
https://www.tensorflow.org/programmers_guide/datasets
You can start your Dataset object from the CSV, and use a dataset.map_fn() to load the images using tf.image.decode_jpeg
Since you're doing pose estimation I'll also suggest a nice blog I came across recently that will probably interest you. The topic is segmentation, but pose estimation is quite related.
http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review