Active Learning & its Applications
Active learning also known as query learning is a sub-field of machine learning. It relies on the assumption that if the learning algorithm is allowed to choose the data from which it learns, it will perform better with less training. Active Learning is predominantly used in areas where getting a large amount of annotated data for training is not feasible or extremely expensive. Active learning models aims to overcome the annotation bottleneck by asking queries in the form of unlabelled instances to be labelled by a human. In this way, the framework aims to achieve high accuracy using very less labelled instances resulting in minimization of annotation cost. In the first part of our work, we propose an Active Learning based Image Annotation model. Automatic image annotation is the computer vision task of assigning a set of appropriate textual tags to a novel image. The aim is to eventually bridge the semantic gap of visual and textual representations with the help of these tags. The advantages of the proposed model includes: (a). It is able to output the variable number of tags for images which improves the accuracy. (b). It is effectively able to choose the difficult samples that needs to be manually annotated and thereby reducing the human annotation efforts. Studies on Corel and IAPR TC-12 datasets validate the effectiveness of this model. In the second part of the thesis, we propose an active learning based solution for efficient, scalable and accurate annotations of objects in video sequences. We focus on reducing the human annotation efforts with simultaneous increase in tracking accuracy to get precise, tight bounding boxes around an object of interest. We use a novel combination of two different tracking algorithms to track an object in the whole video sequence. We propose a sampling strategy to sample the most informative frame which is given for human annotation. This newly annotated frame is used to update the previous annotations. Thus, by collaborative efforts of both human and the system we obtain accurate annotations with minimal effort. We have quantitatively and qualitatively validated the results on eight different datasets. Active Learning is efficient in Natural Language documents as well. Multilingual processing tasks like statistical machine translation and cross language information retrieval rely mainly on availability of accurate parallel corpora. In the third section we propose a simple yet efficient method to generate huge amount of reasonably accurate parallel corpus using OCR with minimal user efforts. We show the performance of our proposed method on a manually aligned dataset of 300 Hindi-English sentences and 100 English-Malayalam sentences. In the last section we utilised Active Learning for model updation in c QA system. Community Question Answering(c QA ) platforms like Yahoo! Answers, Baidu Zhidao, Quora, StackOverflow etc. provides experts to give precise and targeted answers to any question posted by a user. These sites form huge repositories of information in the form of questions and answers. Retrieval of semantically relevant questions and answers from c QA forums have been an important research area for the past few years. Considering the ever growing nature of the data in c QA forums, these models cannot be kept stagnant. They need to be continuously updated so that they can adapt to the changing patterns of Questions-Answers with time. Such updation procedures are expensive and time consuming. We propose a novel Topic model based active sampler named Picky. It intelligently selects a smaller subset of the newly added Question-Answer pairs to be fed to the existing model for updating it. Evaluations on real life c QA datasets show that our approach converges at a faster rate, giving comparable performance to other baseline sampling strategies updated with data of ten times the size.
|Year of completion:||July 2017|
|Advisor :||C V Jawahar|
Priyam Bakliwal, C.V. Jawahar - Active Learning Based Image Annotation Proceedings of the Fifth National Conference on Computer Vision Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2015), 16-19 Dec 2015, Patna, India. [PDF]