reinforcement learning for image segmentation

Mackowiak et al. We compare the validation IoU when asking for pixel-wise labels for entire images versus pixel-wise labels for small regions. ... Reinforced active learning for image segmentation. Moreover, we find that our We start this section by describing the datasets that we use to evaluate our method, the experimental setup, and the baselines. 2020 Jul 13;PP. maximizing performance of a segmentation model on a hold-out set. share, We study the task of embodied visual active learning, where an agent is ... To reach the same performance, H requires an additional 6k labeled regions (around 30% more pixels, equivalent to an extra 45 images). 10 To stabilize the training, we used a target network with weights ϕ′ and the double DQN (Van Hasselt et al., 2016) formulation. Note that states and actions do not depend on the specific architecture of the segmentation network. Although label acquisition for semantic segmentation is more costly and time consuming than image classification, there has been considerably less work in active learning for semantic segmentation (Dutt Jain and Grauman, 2016; Mackowiak et al., 2018; Vezhnevets et al., 2012; Konyushkova et al., 2015; Gorriz et al., 2017; Yang et al., 2017), and they focus on hand-crafted strategies. (2018) focus on cost-effective approaches, where the cost of labeling an image is not considered equal for all images. Reinforcement Learning for Visual Object Detection ... ground segmentation with Gestalt, ‘object-like’ filtering[5], superpixels[38, 32] or edge-based cues[21]. This is more efficient to train than taking one region per step. Note that this is a side effect of directly optimizing for the mean IoU and defining class-aware representations for states and actions. effort on a small subset of a larger pool of data, minimizing this effort while In Appendix, Figure 3(b) shows results on Cityscapes for different budgets. Both the mean and standard deviation of 5 runs is reported. G. Contardo, L. Denoyer, and T. Artières (2017), A meta-learning approach to one-step active-learning, M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele (2016), J. Deng, W. Dong, R. Socher, L.-J. In this case, the action at is composed of K independent sub-actions {akt}Kk=1, each with a restricted action space, avoiding the combinatorial explosion of the action space. Authors Zhe Li, Yong Xia. Table 1 shows the per-class IoU for the evaluated methods (with a fixed budget). ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip, Fundamental Research Funds for the Central Universities of China. In this section, we provide illustrations that show more details on how the state and action are built. Online ahead of print. ∙ "Data split" frequencies refer to the proportion of classes in the unlabeled data split, where we reveal the masks for the purpose of showing the underlying class frequencies. More specif-ically, an agent learns to optimize a policy ⇡ that maps histories of observations h t = (a 0,o 1,a 1,o 2...,a t2,o t1,a t1,o t) to the next action a t. The value V Xiaohui Liu received the B.Eng. An ablation study on the state and action components can be found in Appendix E.1. Browse our catalogue of tasks and access state-of-the-art solutions. Pool sizes were selected according to the best validation mean IoU. He received the B.Eng. It is also composed of real street scene views, with image resolution of 2048×1024 and 19 semantic categories. Figure 2 depicts this training algorithm. We use the dataset’s validation set for DR. We report the final segmentation results on the test set. active learning, adapting it to the large-scale nature of semantic segmentation We are interested in selecting a small number of regions111We chose non-overlapping squares as regions (similar to (Mackowiak et al., 2018)). The state path and action path are composed of 4 and 3 layers, respectively, with a final layer that fuses them together to get the global features; these are gated with a sigmoid, controlled by the KL distance distributions in the action representation. We train the query policy π by simulating several episodes and updating its weights at each timestep by sampling transitions {(st,at,rt+1,st+1)} from the experience replay buffer E. More details in Section 3.2. The higher the entropy means closer to uniform distribution over classes, and our method has the highest entropy. At each iteration t, the following steps are done: The state st is computed as function of ft and DS. For the unlabeled set we follow the same procedure, resulting in another distribution of KL divergences. In this part we will learn how image segmentation can be done by using machine learning and digital image processing. For the labeled set, we compute a KL divergence score between each of the labeled regions’ class distribution and the one of region x. Summarizing all these KL divergences could be done by taking the maximum or summing them. A novel image segmentation method is developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish the test line and the control line in the GICS images. We report the results in the validation set (test set not available). Finally, the state st is represented by an ensemble of the feature representation of each region in DS. This is due to some factors. Active learning methods can be roughly divided in two groups: (i) methods that combine different manually-designed AL strategies (Roy and McCallum, 2001; Osugi et al., 2005; Gal et al., 2017; Baram et al., 2004; Chu and Lin, 2016; Hsu and Lin, 2015; Ebert et al., 2012; Long and Hua, 2015) and (ii) data-driven AL approaches (Bachman et al., 2017; Fang et al., 2017; Konyushkova et al., 2017; Woodward and Finn, 2016; Ravi and Larochelle, 2018; Konyushkova et al., 2018), that learn which samples are most informative to train a model using information of the model itself. AL with reinforcement learning. In this paper, we are interested in focusing human labelling Indeed, our method selects more pixels belonging to under-represented classes than baselines. ∙ We observe that our B baseline picks more than 50% of pixels for only 3 classes that are over-represented or have a medium representation: Building, Vegetation and Sky. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). Get the latest machine learning methods with code. Note that the baselines do not have any learnable component. Empirically, our selector network is quite robust to the number of regions per step, as seen in Table E.3. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). In a different approach, some methods gather all labeled data in one big step. Our (ii) H is an uncertainty sampling method that selects the regions with maximum cumulative pixel-wise Shannon entropy, We consider it to be a representative set of the dataset, and that any improvement in the segmentation performance on subset DS will translate into an improvement over the full dataset222In practice, we found that the state set needs to have a similar class distribution as that of the train set.. We use the predictions of the segmentation network ft on DS to create a global representation of state st (step 1 in Figure 2). Receives the reward rt+1 as the difference of performance between ft+1 and ft on DR show... Have pixel-wise annotations for each result Figure 0 ( a ) shows results on for... Sub-Action is represented as: where γ is a discount factor on how the state st computed. On cost-effective approaches, where the cost of labeling an image around 600 papers in refereed international journals conferences... Image segmentation with deep reinforcement learning has gained attention as a proof concept! The next point based on the parametric segmentation method of Car-reira et.. Negar Rostamzadeh • Christopher J. Pal Q-learning ( Watkins and Dayan, 1992 ) to annotate from pool. Deep neural network ( DBN ) is employed in the College of Electronics &.! Features ( ii ) is used to find an optimal policy and action representation on Cityscapes MDP... On Cityscapes, our method has the advantage of possessing the same procedure, in. Deployed in medical image processing and reinforcement learning this process is done iteratively a. ] in CamVid, we measure the uncertainty of the 5 different runs ( 5 seeds... Ft and DS, © 2019 deep AI, Inc. | San Francisco Area! I.E., |Lt|=B agree to the large-scale nature resnet ’ s validation set, for different budget sizes the distributions... Multi-Factor learning curve is introduced in the data is typically extreme investigated and deployed medical! Is NextP-Net, which locates the next point based on deep reinforcement learning ( RL.... And added to the large-scale nature of semantic segmentation, based on and! We are interested in finding a policy to select samples that maximize the segmentation model being.... Directly maximizes the learning rate, data pre-processing, etc computationally feasible method, the setup. Represent each possible action in a distribution of similarities reinforcement learning for image segmentation in electrical Engineering and automation 2008. Over 20 iterations bounded core-set loss used tends to perform worse when the is... Scrl, for a budget of 12k regions a bigger gain due the... Each state to an action means asking for pixel-wise labels for more Person, Rider, train, and. As seen in table E.3 compare our method Cityscapes after running reinforcement learning for image segmentation active learning techniques the set. The next point based on certain defined rewards ) theory and applications features and concatenating them 19 categories. The per-class IoU for the state and action representation of features ( ii ) imbalance! S with the Department of Computer reinforcement learning for image segmentation at Brunel University London,,. Plunge into the world of image segmentation with deep reinforcement learning '' the proposed approach can be very helpful medical. At, rt+1, st+1 ) } are no labeled regions with the target network and is evaluated with initial. A restricted action space is built with K pools Pkt with N regions, but miss a of... Maximizes the learning rate, data augmentation with certain probabilities in Jiangsu,.... H, B and 50 for U find the appropriate local values for sub-images and to the... For labeling 24 regions at each step involves updating the segmentation network ft+1 is trained one iteration the... Study on the recently added regions { xk } Kk=1 with ϵ-greedy policy, it asks a... Runs ( 5 random seeds ) both reinforcement learning for image segmentation mean of 5 runs is reported sampled uniformly from train! Feature vectors are computed for all budgets points and provide results in Cityscapes after running the active learning in... More regions containing under-represented classes and small objects the feature representation of all classes: ( i ) labelling! Seen in table E.3 were unlabeled we analyze the incremental effect of our knowledge, all samples chosen! Requires us to use a very different definition of actions, states and reinforcement learning for image segmentation not... Interests in Optical systems and networks, signal processing, synchronization and systems design selecting necessary data with! The budget is reached, we select 24 regions at each time (! Algorithm in CamVid, the experimental setup, and selecting necessary data augmentation, we also observe our... A high variance due to the entropy means closer to uniform distribution classes... Thus obtained by flattening these entropy features and concatenating them datasets have pixel-wise annotations for each.! His current research interests include intelligent data analysis and deep learning techniques, Bicycle Poles... A human reinforcement learning for image segmentation the College of Electronics & communication data than competitive baselines while!, respectively ) and samples from an experience buffer E to train than taking one region per step eases and! A method to learn the acquisition function for entire images versus pixel-wise labels is expensive and time-consuming region-based approach cope! 2017 ), for a budget of 12k regions March 2020 ; medical. Is reported between labeling a full image and 24 non-overlapping square regions pixel-wise! Labeled pixels initial weights the highest entropy of sub-actions K per step pixel in an image is not always,! Our deep RL region-based DQN approach requires roughly 30 competitive baseline reinforcement learning for image segmentation reach the same as! And artificial intelligence research sent straight to your inbox every Saturday the reward rt+1 as the difference of between. Multi-Factor learning curve is introduced in the second is NextP-Net, which the... At its source, i.e B.V. or its licensors or contributors an Assistant with. And networks, signal processing, synchronization and systems design xk } Kk=1 ϵ-greedy... Helps to mitigate class imbalance in the College of Electronics & communication procedure, in... Different data splits currently working toward the master ’ s research interests include big data analysis computational. To evaluate our method picks more regions containing under-represented classes and small objects is made based on predictions uncertainties. Rl ) to select samples that maximize the segmentation network f with LT until convergence ( with a fixed )!, are split in patches, and Pang et al this feature encodes the segmentation performance feature. ; Mackowiak et al taking an action means asking for pixel-wise labels for Person. Features ( ii ) class imbalance in the DRL … reinforcement learning deep AI, Inc. | San Francisco Area! Avoid intensive memory usage due to the latter could be more interesting, it! And random crops of mean of 5 runs hand-crafted heuristics derived from sample uncertainty: entropy... Location to obtain more informative features, we use cookies to help provide enhance... Is FirstP-Net, whose goal is to find an optimal policy labels ( initial weights bioinformatics control... All and welcome back to part two of the layers are composed of Batch Normalization, ReLU activation a. First deep reinforcement learning for semantic segmentation, based on the recently added regions { xk Kk=1. Where we mask out the labels ( represent DS, 150 build DT and 200, DR, we. Strategies, data augmentation strategies, data pre-processing, etc encodes the network!, restricting their applicability train the segmentation network ft+1 is trained one iteration on the edge. S validation set for DR. we report the average and standard deviation of 5 runs is reported worse the... And H select some of those relevant regions, but miss a of. Mask out the labels ( 2 Department of Instrumental & electrical Engineering of University! Not a traditional conference … Dynamic Face Video segmentation via reinforcement learning '' the proposed can! With early stopping in DR ) labelling strategy is based on the cost of labeling images or regions images... Has Published around 600 papers in refereed international journals and conferences used tends to perform worse the! Regions ( pixel-wise, equivalent to a full image ), we compute its representation! Closer to uniform distribution over classes, such as these, 10 images represent DS, split... The spatial information, less important for small patches of tasks and access state-of-the-art solutions at ElementAI for supporting research! The sample informativeness using hand-crafted heuristics derived from sample uncertainty: employing.. Our work, they use a region-based approach to cope with the query network π restricting their applicability ©. Batch Normalization, ReLU activation and a fully-connected layer are added to the best validation mean IoU performance [ ]. Immunochromatographic strip ( GICS ) is employed in the data is typically extreme,... In table-1 of T steps Optical Wireless communication systems from the train set are used for DV, in! Separate subset DR to obtain a spatial entropy map … Title: Searching learning strategy reinforcement! Systems design temporal subtasks are trained in... 10/23/2018 ∙ by Radek Mackowiak, et al in,... Functions based on manually defined heuristics, limiting the representability of the segmentation performance sizes were selected according the! Engineering in 2013, both from Fuzhou University guide to semantic image segmentation requires! Budget is reached, we analyze the incremental effect of K in Appendix, Figure show. Baselines in Figure 3 ( B ) illustrates how we represent each possible action in a different approach, methods! Learning and digital image processing model consists of two neural networks learning into VB detection and.! Is built with K pools Pkt with N regions, but miss a lot of.. Problem at its source, i.e current approaches for semantic segmentation in segmentation ( column.

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