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: A scheme for training sample set management based on similarity learning.

Keyphrases: visual tracking images

Dataset description:
SFEW: The Static Facial Expressions in the Wild (SFEW) dataset is a dataset for facial expression recognition. It was created by selecting static frames from the AFEW database by computing key frames based on facial point clustering.
BIOSSES: The BIOSSES data set comprises total 100 sentence pairs all of which were selected from the "TAC2 Biomedical Summarization Track Training Data Set" . The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores in a range [0-4].
Color FERET: The color FERET database is a dataset for face recognition. It contains 11,338 color images of size 512×768 pixels captured in a semi-controlled environment with 13 different poses from 994 subjects.
OTB: Object Tracking Benchmark (OTB) is a visual tracking benchmark that is widely used to evaluate the performance of a visual tracking algorithm. The dataset contains a total of 100 sequences and each is annotated frame-by-frame with bounding boxes and 11 challenge attributes.
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.
UNITOPATHO: Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade.
ImageNet-Sketch: ImageNet-Sketch data set consists of 50000 images, 50 images for each of the 1000 ImageNet classes. The data set is constructed with Google Image queries "sketch of ", where  is the standard class name.

Options:- A: SFEW  B: BIOSSES  C: Color FERET  D: OTB  E: ImageNet  F: UNITOPATHO  G: ImageNet-Sketch
["OTB", "ImageNet"]