You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: This paper proposes an effective way to aggregate local binary features, and combines them with deep

Keyphrases: content-based image retrieval images

Dataset description:
Diabetic Retinopathy Detection Dataset: A large scale of retina image dataset.
REDDIT-BINARY: REDDIT-BINARY consists of graphs corresponding to online discussions on Reddit. In each graph, nodes represent users, and there is an edge between them if at least one of them respond to the other’s comment.
IMDB-BINARY: IMDB-BINARY is a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie.
PA-100K: PA-100K is a recent-proposed large pedestrian attribute dataset, with 100,000 images in total collected from outdoor surveillance cameras. It is split into 80,000 images for the training set, and 10,000 for the validation set and 10,000 for the test set.
Oxford5k: Oxford5K is the Oxford Buildings Dataset, which contains 5062 images collected from Flickr. It offers a set of 55 queries for 11 landmark buildings, five for each landmark.
ImageNet: The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection.
PMLB: The Penn Machine Learning Benchmarks (PMLB) is a large, curated set of benchmark datasets used to evaluate and compare supervised machine learning algorithms. These datasets cover a broad range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features.

Options:- A: Diabetic Retinopathy Detection Dataset  B: REDDIT-BINARY  C: IMDB-BINARY  D: PA-100K  E: Oxford5k  F: ImageNet  G: PMLB
["Oxford5k", "ImageNet"]