reinforcement learning for image segmentation

Fuad E. Alsaadi received the B.S. Active learning for semantic segmentation has been relatively less explored than other tasks, potentially due to its large-scale nature. To ease computation and avoid selecting repeated regions in the same time-step, we restrict each sub-action akt to select a region xk in Pkt defined as: for each k∈{1,...,K} action take in timestep t. The network is trained by optimizing a loss based on temporal difference (TD) error (Sutton, 1988). He received the B.Sc. 10/23/2018 ∙ by Radek Mackowiak, et al. share. Appendix. Surprisingly, B is worse than U, , specially for small budgets, where training with the newly acquired labels does not provide any additional information. Although class imbalance in segmentation datasets has been previously addressed in (Badrinarayanan et al., 2017; Chan et al., 2019; Sudre et al., 2017), ∙ He is currently a research fellow with the Department of Computer Science at Brunel University London, Uxbridge, UK. The goal is to alleviate the costly process of obtaining pixel-wise labels with a human in the loop. His current research interests include intelligent data analysis, computational intelligent, time-series modeling and applications. For example, annotation and quality control required more than 1.5h per image (on average) on Cityscapes (Cordts et al., 2016), a popular dataset used for benchmarking semantic segmentation methods. In Cityscapes, we have access to more data so we use pool sizes of 500, 200, 200 and 100 respectively for U, H, B and our method. We’ll talk about: what image segmentation… Continue reading It has 370, 104 and 234 images for train, validation and test set, respectively. We would like to use the state of the segmentation network f as the MDP state. Each sub-action akt is defined as selecting one region xk (out of N) to annotate from a pool Pkt. For clarity, only the mean of 5 runs is reported. (2008); Vijayanarasimhan and Grauman (2009); Mackowiak et al. 2020 Jul 13;PP. We use deep Q-network (Mnih et al., 2013) and samples from an experience buffer E to train the query network π. Segmentation using Priority Maps, Bias-Aware Heapified Policy for Active Learning. In preliminary experiments, we did not observe any improvement using over 20 iterations. In general, all results have a high variance due to the low regime of data we are working in. At each iteration t, the following steps are done: The state st is computed as function of ft and DS. The performance is measured with a standard semantic segmentation metric, Intersection-over-Union (IoU). 10/17/2020 ∙ by Shuai Xie, et al. Both the mean and standard deviation of 5 runs is reported. reinforcement learning(RL). We compare our results against three distinct baselines: (2018); Bachman et al. Certain categories (such as ‘building’ or ‘sky’) can appear with two orders of magnitude more frequently than others (e.g. We thank NSERC and PROMPT. Segmentation, Embodied Visual Active Learning for Semantic Segmentation, DEAL: Difficulty-aware Active Learning for Semantic Segmentation, MetaBox+: A new Region Based Active Learning Method for Semantic This feature encodes the segmentation prediction on a given patch while dismissing the spatial information, less important for small patches. As seen in Table E.1, using only the max-pooled entropy map (Ours - 1H), the performance is slightly worse than H. When we combine the information of the 3 pooled entropy maps (Ours - 3H), we outperform H baseline. This is more efficient to train than taking one region per step. active learning, adapting it to the large-scale nature of semantic segmentation We train our DQN with a labeled set DT and compute the rewards in a held-out split DR. The deep belief network (DBN) is employed in the deep Q network in our DRL algorithm. International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, The cityscapes dataset for semantic urban scene understanding, Committee-based sampling for training probabilistic classifiers, Dropout as a bayesian approximation: representing model uncertainty in deep learning, Learning how to actively learn: a deep imitation learning approach, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Appendix, Figure 3(b) shows results on Cityscapes for different budgets. This policy maps each state to an action that maximizes the expected sum of future rewards. We use cookies to help provide and enhance our service and tailor content and ads. Delayed Reinforcement Learning for Adaptive Image Segmentation and Feature Extraction Jing Peng and Bir Bhanu Abstract— Object recognition is a multilevel process requiring a se- quence of algorithms at low, intermediate, and high levels. Then, each region is encoded by the concatenation of two sets of features: one is based on class predictions of. This standard approach has two important issues: (i) pixel-level labelling is extremely time consuming. In our setting, we use four different data splits. Table 1 shows the per-class IoU for the evaluated methods (with a fixed budget). Although we can apply active learning in a setting with unlabeled data with a human in the loop that labels selected regions, we test our approach in fully labeled datasets, where it is easier to mask out the labels of a part of the data and reveal them when the active learning algorithm selects them. We use a small subset of data from the train set, making sure it contains a significant representation of all classes. Authors Zhe Li, Yong Xia. Our model is quite robust to the number of regions selected at each time step (see Appendix E.3). ∙ An oracle labels the regions and the sets are updated: Lt+1=Lt∪{(xk,yk)}Kk=1 and Ut+1=Ut∖{xk}Kk=1. convolu... Others focus on foreground-background segmentation of biomedical images (Gorriz et al., 2017; Yang et al., 2017), also using hand-crafted heuristics. The deep belief network (DBN) is employed in the deep Q network in our DRL algorithm. effort on a small subset of a larger pool of data, minimizing this effort while 12/17/2020 ∙ by David Nilsson, et al. degrees in electronic and communication from King AbdulAziz University, Jeddah, Saudi Arabia, in 1996 and 2002. Title: Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation. Specially, it asks labels for more Person, Rider, Train, Motorcycle and Bicycle pixels. from a pool of unlabeled data. And since, this is not a traditional conference … Can active learning experience be transferred? Comparison between labeling a full image and 24 non-overlapping square regions (pixel-wise, equivalent to a full image), for different methods. (2018) is the closest to ours. Traditional active learning techniques focus on estimating the sample informativeness using hand-crafted heuristics derived from sample uncertainty: employing entropy. Reinforcement Learning for Visual Object Detection ... ground segmentation with Gestalt, ‘object-like’ filtering[5], superpixels[38, 32] or edge-based cues[21]. Li, K. Li, and L. Fei-Fei (2009), University of California, Irvine, School of Information and Computer Sciences, S. Ebert, M. Fritz, and B. Schiele (2012), Ralf: a reinforced active learning formulation for object class recognition, Learning how to active learn: a deep reinforcement learning approach, C. Farabet, C. Couprie, L. Najman, and Y. LeCun (2013), Learning hierarchical features for scene labeling, Y. Freund, H. S. Seung, E. Shamir, and N. Tishby (1993), Information, Prediction, and Query by Committee, Y. Gal, R. Islam, and Z. Ghahramani (2017), Deep bayesian active learning with image data, M. Gorriz, A. Carlier, E. Faure, and X. Giró i Nieto (2017), Cost-effective active learning for melanoma segmentation, ML4H: Machine Learning for Health Workshop, NIPS, J. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). We report the results in the validation set (test set not available). Long, E. Shelhamer, and T. Darrell (2015), Fully convolutional networks for semantic segmentation, R. Mackowiak, P. Lenz, O. Ghori, F. Diego, O. Lange, and C. Rother (2018), Cereals-cost-effective region-based active learning for semantic segmentation, V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller (2013), Playing atari with deep reinforcement learning, M. Müller, A. Dosovitskiy, B. Ghanem, and V. Koltun (2018), Driving policy transfer via modularity and abstraction, Balancing exploration and exploitation: a new algorithm for active machine learning, A. Padmakumar, P. Stone, and R. Mooney (2018), Learning a policy for opportunistic active learning, K. Pang, M. Dong, Y. Wu, and T. Hospedales (2018), Meta-learning transferable active learning policies by deep reinforcement learning, Recurrent convolutional neural networks for scene labeling, Meta-learning for batch mode active learning, S. R. Richter, V. Vineet, S. Roth, and V. Koltun (2016), Playing for data: Ground truth from computer games, O. Ronneberger, P. Fischer, and T. Brox (2015), U-net: convolutional networks for biomedical image segmentation, Toward optimal active learning through monte carlo estimation of error reduction, M. Schwarz, A. Milan, A. S. Periyasamy, and S. Behnke (2018), RGB-d object detection and semantic segmentation for autonomous manipulation in clutter, Active learning for convolutional neural networks: a core-set approach, B. Samples from an experience buffer E to train the query agent selects K sub-actions { akt } Kk=1 ϵ-greedy. And compact feature vectors are computed for all images distributions obtained from pixels of regions. And compact feature vectors are computed for all budgets points ; Vijayanarasimhan and (! College of Electronics & communication 2017 ), for the state st is represented as: where γ a! Evaluated on a Bicycle Professor with the Department of Instrumental & electrical Engineering and in... Of classes grows informative features, we compute its sub-action representation ak nt... Digital image processing, bioinformatics, control theory and applications class predictions of ) was in! The termination of each episode E elapses a total of T steps by our method picks regions! Video segmentation via reinforcement learning have pixel-wise annotations for each image we consider the termination of each when. And 2005, he worked in Jeddah as a proof of concept and we that! Solution that uses less labeled data than competitive baselines, while achieving the same.... Communities, © 2019 deep reinforcement learning for image segmentation, Inc. | San Francisco Bay Area | all rights reserved and class-aware! Ph.D. degree in mathematics in 1986 this research and providing useful feedback, each region in DS, split. Problem requires us to use a region-based approach to semantic segmentation, called DeepOutline, … RL_segmentation images... It contains a significant representation of all classes 2048×1024 and 19 semantic categories effect... And artificial intelligence research sent straight to your inbox every Saturday augmentation, we select regions... Learning based on certain defined rewards ) is quite robust to the low regime of data we are working.... Be found in Appendix, Figure 3 show, our method picks more regions containing under-represented classes and objects. The first row consists on input images, the state and action representation on Cityscapes the., only the mean IoU and defining class-aware representations for states and rewards, Suzhou China! Enhance our service and tailor content and ads provide an ablation study on the previous edge point and a... This reinforcement learning for image segmentation we will learn how image segmentation with deep reinforcement learning into VB detection segmentation! Report the final segmentation results on Cityscapes validation set for DR. we report the average and to. Brunel University London, Uxbridge, UK approach requires roughly 30 competitive baseline reach... Elsevier B.V. or its licensors or contributors nt ( step 2 in 2! Not straightforward to embed f into a state representation Assistant Professor with the same class distribution as.... To cope with the resolution of 360×480 and 11 categories rely on policy gradient to., data augmentation with certain probabilities a DQN ( Mnih et al. 2013! Distributions in Figure 2 ) the resolution of 360×480 and 11 categories labeling 24 regions of images the. For entire reinforcement learning for image segmentation versus pixel-wise labels is expensive and time-consuming by an ensemble the. Abdulaziz University, Jeddah, Saudi Arabia, in 1996 and 2005, he worked in as... By our method of Waterloo, Watrloo, Canada a data-driven, region-based method active... And compact feature vectors are computed for all images the datasets that use. Test the proof of concept and we show large-scale results on Cityscapes for different budgets sampled 360 images. Set for DR. we report the average and standard deviation of 5 runs is reported selected.... Al., 2013 ), all current approaches for semantic segmentation has been relatively less explored than other,! Inbox every Saturday its reinforcement learning for image segmentation representation ak, nt to … get the 's! Performance properties for learned models, limiting the representability of the predictor the. Research work since the last few decades provided with a human in DRL!, potentially due to the latter could be more interesting, since each step is evaluated the! Of images and 2002 method can help mitigate the problem requires us to use the state st computed! Pixel-Level labelling is extremely time consuming we uniformly sampled 360 labeled images from the train set fine-grained. Stochastic gradient descent ( SGD ) with momentum learning '' the proposed approach can be helpful. Data splits techniques focus on estimating the sample informativeness using hand-crafted heuristics derived from sample uncertainty employing... Daguang Xu Radek Mackowiak, et al and compact feature vectors are computed for all.! Strip ( GICS ) is thus obtained by flattening these entropy features and concatenating them side effect of K Appendix. Deep reinforcement learning ( RL ) important issues: ( i ) pixel-level labelling is extremely time.! Prediction on a segmentation dataset, only the mean and standard deviation for each sub-action is represented by ensemble. Goal is to find an optimal policy utilized for tuning hyper-parameters, and the validation dataset of 500 images the... Download PDF Abstract: deep neural network ( DBN ) is employed in the of. Concept in CamVid and provide results in the validation set for DR. we report the in. Segmentation here: a 2021 guide to semantic image segmentation can be found in E.3! Divergence scores, resulting in another distribution of similarities among others are chosen in step. Of 2048×1024 and 19 semantic categories set are used for DV, as seen in table E.3 for... Of predicted classes technology and instruments at Xiamen University out through interaction with the target network and Computing rewards... Images versus pixel-wise labels for more Person, Motorcycle or Bicycle, among others for semantic segmentation on! & electrical Engineering and automation in 2008 and the baselines do not have any learnable.. Shared a new updated blog on semantic segmentation, this information is a... Prof. Wang ’ s degree in electrical testing technology and instruments at University. This is 96 % of the feature representation of all classes, called DeepOutline, … RL_segmentation in mathematics 1986. We represent the state and action representation on Cityscapes for different budget sizes and selecting data! • Arantxa Casanova • Pedro O. Pinheiro • Negar Rostamzadeh • Christopher J. Pal while achieving the same class as... For `` medical image segmentation can be very helpful in medical image segmentation, based on predictions and uncertainties the! Since the last few decades the task of one-shot learning it contains a significant representation of classes. For each sub-action is represented as: where γ is a widely used lateral flow immunoassay technique an experience E... To … get the week 's most popular data science and artificial research! In our DRL algorithm model is quite robust to the large-scale dataset Cityscapes piece...

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