how I can find valid link for download BATH800 dataset?
"This dataset is a collection of eye images"
I tried to find the download link through the references given in different articles.
But these links have problems and do not work.
Related
I've started a project titled "Multi-Modal (Image, Audio, Text) Analysis of Personality Traits", I need refernces to dataset that has image , audio and speech annotation of video which are also labelled w.r.t "Big Five Personality Traits", Thank you in advance!
I tried searching many research papers, most of them didn't have their code open or their data didn't meet my requirement and I tried to access chalearn first impression dataset but couldn't get access to it.
I am trying to understand openface.
I have installed the docker container, run the demos and read the docks.
What I am missing is, how to start using it correctly.
Let me explain to you my goals:
I have an app on a raspberry pi with a webcam. If I start the app it will take a picture of the person infront.
Now it should send this picture to my openface app and check, if the face is known. Known in this context means, that I already added pictures of this person to openface before.
My questions are:
Do I need to train openface before, or could I just put the images of the persons in a directory or s.th. and compare the webcam picture on the fly with these directories?
Do I compare with images or with a generated .pkl?
Do I need to train openface for each new person?
It feels like I am missing a big thing that makes the required workflow clearer to me.
Just for the record: With help of the link I mentioned I could figure it out somehow.
Do I need to train openface before, or could I just put the images of the persons in a directory or s.th. and compare the webcam picture on the fly with these directories?
Yes, a training is required in order to compare any images.
Do I compare with images or with a generated .pkl?
Images are compared with the generated classifier pkl.
The pkl file gets generated when training openface:
./demos/classifier.py train ./generated-embeddings/
This will generate a new file called ./generated-embeddings/classifier.pkl. This file has the SVM model you'll use to recognize new faces.
Do I need to train openface for each new person?
OK, for this question I don't have an answer yet. But just because I did not look deeper into this topic yet.
I run a download portal and basically what I want to do is after a user downloads a file i would like to recommend other related categories. I'm thinking of using google predict to do this but I'm not sure how to structure the training data. I'm thinking something like this:
category of the file downloaded (label), geo, gender, age
however that seems incomplete because the data doesn't have any information on the file downloaded. Would appreciate some advice, new to ML.
Here is a suggestion that might work...
For your training data, assuming you have the logs of downloads per user, create the following dataset:
download2 (serves as label), download1, features of users, features of download1
Then train a classifier to predict class given a download and user - the output classes and corresponding scores represent downloads to recommend.
I can't find image files for WDRef dataset. Where to get it?
In publication authors wrote:
To address this issue, we introduce a new
dataset, Wide and Deep Reference dataset (WDRef), which is both wide (around
3,000 subjects) and deep (2,000+ subjects with over 15 images, 1,000+ subjects
with more than 40 images). To facilitate further research and evaluation on
supervised methods on the same test bed, we also share two kinds of extracted
low-level features of this dataset. The whole dataset can be downloaded from
our project website http://home.ustc.edu.cn/~chendong/JointBayesian/.
But, there are only LE and LBP features on their website.
Only features are available for WDRef dataset.
Another large dataset is here:
http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html
Also on this webpage, it is confirmed that WDRef is feature only public.
I am trying to build a news aggregation system where I will have to process web pages from new news portals everyday. How can I extract the main image of the news article from the webpages without writing html extraction handlers for each portal. How can I guess which is the main image of an article when most of the pages will have 10-15 random ads and side images in it. I tried selecting the largest image in each page but that did not work out well and gave many false positives
There is no such thing as a "main" image on the site. This concept is fully context dependent, in terms of news it could be "the image related to the text", but this is very specific situation - what if there are many images inside the news showing some situation?
As it is very hard to define what you really mean, the machine learning based approach seems reasonable, as "learning by example" should be easier to do.
I would extract the most promising features of each image:
Its relative size to others
Its distance from the news container in the DOM of the webpage
Whether its name containes keywords like "news"; "main"
whether it does not contain "bad" keywords like "ad", "logo", "menu"
And then train the simplest possible classifier on it (Bayes or Logistic Regression) on some collected set of samples.