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Welcome to ShortScience.org! |
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- ShortScience.org is a platform for post-publication discussion aiming to improve accessibility and reproducibility of research ideas.
- The website has 1584 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
- Reading summaries of papers is useful to obtain the perspective and insight of another reader, why they liked or disliked it, and their attempt to demystify complicated sections.
- Also, writing summaries is a good exercise to understand the content of a paper because you are forced to challenge your assumptions when explaining it.
- Finally, you can keep up to date with the flood of research by reading the latest summaries on our Twitter and Facebook pages.
STACS: new active contour scheme for cardiac MR image segmentation
Pluempitiwiriyawej, Charnchai and Moura, José M. F. and Wu, Yi-Jen Lin and Ho, Chien
IEEE Trans. Med. Imaging - 2005 via Local Bibsonomy
Keywords: dblp
Pluempitiwiriyawej, Charnchai and Moura, José M. F. and Wu, Yi-Jen Lin and Ho, Chien
IEEE Trans. Med. Imaging - 2005 via Local Bibsonomy
Keywords: dblp
[link]
Automated segmentation of various anatomical structures of interest from medical images has been a well grounded field of research in medical imaging. One such problem is related to segmenting whole heart region from a sequence of magnetic resonance imaging (MRI), which is currently done manually, and is time consuming and tedious. Although many automated techniques exist for this, the task remains challenging due to the complex nature of the problem, partly because of low contrast between heart and nearby tissue. Moreover many of the methods are unable to incorporate prior information into the process. To this end, Pluempitiwiriyawej et al. proposed a version of active contour energy minimization based method to segment the whole heart region, including the epicardium, and the left and right ventricular endocardia.
The proposed method follows the framework laid out by Chan and Vese\cite{Chan2001}. However Pluempitiwiriyawej et al. propose a modified energy function, which consists of four energy terms. The energy function is given below, where $C$ is the contour represented as a level set function $\phi(x,y)$:
$J(C) = \lambda_1 J_1(C) + \lambda_2 J_2(C) + \lambda_3 J_3(C) + \lambda_4 J_4(C)$
The coefficients $\lambda_{1..4}$ determine the weight of terms $J_{1..4}$. The first term $J_1(C)$ is designed to add stochastic models $\mathcal{M}_1, \mathcal{M}_2$ corresponding to the regions inside and outside of the active contour $C$. The models dictate the probability distribution from which the image intensities making up the inside and outside region of the contour are sampled. The negative log of this term is minimized, which essentially maximizes the probability $p(u | C, \mathcal{M}_1, \mathcal{M}_2)$ given the active contour $C$, and the models $\mathcal{M}_1, \mathcal{M}_2$.
The second term $J_2(C)$ is designed similar to the classical Snakes\cite{Kass1988} in the sense that it uses edges to guide the contour towards the structure of interest. For this term, a simple edge map is used after convolving with a Gaussian filter which smooths out the noise. The term $J_3(C)$ encodes an shape prior which constraints the contour to follow an elliptical shape, and guides it in conjunction with the region and edge information.
The final term $J_4(C)$ which encodes the total Euclidean arc length of the contour. This forces the contour to be ``smooth", without rough edges.
The process of minimizing the energy function follows a three-task approach. The first task is to estimate the stochastic model parameters $\mu_k, \sigma^2_k$, and is performed by fixing the position of initial contour $C$, taking derivatives of $J$ w.r.t stochastic model parameters, and solving by equating to zero. The second task estimates the parameters of the ellipse using least squares method. The third and final task involves the contour using the estimated parameters in task one and two, such that it minimizes the function $J$. The method also performs stochastic relaxation, by dynamically changing the values of parameters $\lambda_1, \lambda_2, \lambda_3, \lambda_4$ as the optimization process proceeds. The intuition is that when the optimization starts, the edge and region terms must guide the contour, and as the process proceeds to it's end, the shape prior and contour length term should carry more weight to regularize the effective shape of the contour.
The study used 48 MRI studies acquired by imaging rat hearts, and compared the proposed method with two earlier methods, namely Xu and Prince's GVF \cite{ChenyangXu1998}, and Chan and Vese \cite{Chan2001}. The authors also design a new quantitative metric, which is a modification of the Chamfer matching \cite{Barrow} technique. The reported results are observed to be in excellent agreement with the gold standard hand-traced contours. However the similarity values for other methods against human gold-standard were not reported.
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.LG, stat.ML
First published: 2016/09/09 (9 years ago)
Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.
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Thomas N. Kipf and Max Welling
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.LG, stat.ML
First published: 2016/09/09 (9 years ago)
Abstract: We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.
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[link]
The propagation rule used in this paper is the following:
$$
H^l = \sigma \left(\tilde{D}^{-\frac{1}{2}} \tilde{A} \tilde{D}^{-\frac{1}{2}} H^{l-1} W^l \right)
$$
Where $\tilde{A}$ is the [adjacency matrix][adj] of the undirected graph (with self connections, so has a diagonal of 1s and is symmetric) and $H^l$ are the hidden activations at layer $l$. The $D$ matrices are performing row normalisation, $\tilde{D}^{-\frac{1}{2}} \tilde{A} \tilde{D}^{-\frac{1}{2}}$ is [equivalent to][pygcn] (with $\tilde{A}$ as `mx`):
```
rowsum = np.array(mx.sum(1)) # sum along rows
r_inv = np.power(rowsum, -1).flatten() # 1./rowsum elementwise
r_mat_inv = sp.diags(r_inv) # cast to sparse diagonal matrix
mx = r_mat_inv.dot(mx) # sparse matrix multiply
```
The symmetric way to express this is part of the [symmetric normalized Laplacian][laplace]. This work, and the [other][hammond] [work][defferrard] on graph convolutional networks, is motivated by convolving a parametric filter over $x$. Convolution becomes easy if we can perform a *graph Fourier transform* (don't worry I don't understand this either), but that requires us having access to eigenvectors of the normalized graph Laplacian (which is expensive).
[Hammond's early paper][hammond] suggested getting around this problem by using Chebyshev polynomials for the approximation. This paper takes the approximation even further, using only *first order* Chebyshev polynomial, on the assumption that this will be fine because the modeling capacity can be picked up by the deep neural network.
That's how the propagation rule above is derived, but we don't really need to remember the details. In practice $\tilde{D}^{-\frac{1}{2}} \tilde{A} \tilde{D}^{-\frac{1}{2}} = \hat{A}$ is calculated prior and using a graph convolutional network involves multiplying your activations by this sparse matrix at every hidden layer. If you're thinking in terms of a batch with $N$ examples and $D$ features, this multiplication happens *over the examples*, mixing datapoints together (according to the graph structure). If you want to think of this in an orthodox deep learning way, it's the following:
```
activations = F.linear(H_lm1, W_l) # (N,D)
activations = activations.permute(1,0) # (D,N)
activations = F.linear(activations, hat_A) # (D,N)
activations = activations.permute(1,0) # (N,D)
H_l = F.relu(activations)
```
**Won't this be really slow though, $\hat{A}$ is $(N,N)$!** Yes, if you implemented it that way it would be very slow. But, many deep learning frameworks support sparse matrix operations ([although maybe not the backward pass][sparse]). Using that, a graph convolutional layer can be implemented [quite easily][pygcnlayer].
**Wait a second, these are batches, not minibatches?** Yup, minibatches are future work.
**What are the experimental results, though?** There are experiments showing this works well for semi-supervised experiments on graphs, as advertised. Also, the many approximations to get the propagation rule at the top are justified by experiment.
**This summary is bad.** Fine, smarter people have written their own posts: [the author's][kipf], [Ferenc's][ferenc].
[adj]: https://en.wikipedia.org/wiki/Adjacency_matrix
[pygcn]: https://github.com/tkipf/pygcn/blob/master/pygcn/utils.py#L56-L63
[laplace]: https://en.wikipedia.org/wiki/Laplacian_matrix#Symmetric_normalized_Laplacian
[hammond]: https://arxiv.org/abs/0912.3848
[defferrard]: https://arxiv.org/abs/1606.09375
[sparse]: https://discuss.pytorch.org/t/does-pytorch-support-autograd-on-sparse-matrix/6156/7
[pygcnlayer]: https://github.com/tkipf/pygcn/blob/master/pygcn/layers.py#L35-L68
[kipf]: https://tkipf.github.io/graph-convolutional-networks/
[ferenc]: http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/
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Dual Learning for Machine Translation
Yingce Xia and Di He and Tao Qin and Liwei Wang and Nenghai Yu and Tie-Yan Liu and Wei-Ying Ma
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CL
First published: 2016/11/01 (9 years ago)
Abstract: While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation \emph{dual-NMT}. Experiments show that dual-NMT works very well on English$\leftrightarrow$French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.
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Yingce Xia and Di He and Tao Qin and Liwei Wang and Nenghai Yu and Tie-Yan Liu and Wei-Ying Ma
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CL
First published: 2016/11/01 (9 years ago)
Abstract: While neural machine translation (NMT) is making good progress in the past two years, tens of millions of bilingual sentence pairs are needed for its training. However, human labeling is very costly. To tackle this training data bottleneck, we develop a dual-learning mechanism, which can enable an NMT system to automatically learn from unlabeled data through a dual-learning game. This mechanism is inspired by the following observation: any machine translation task has a dual task, e.g., English-to-French translation (primal) versus French-to-English translation (dual); the primal and dual tasks can form a closed loop, and generate informative feedback signals to train the translation models, even if without the involvement of a human labeler. In the dual-learning mechanism, we use one agent to represent the model for the primal task and the other agent to represent the model for the dual task, then ask them to teach each other through a reinforcement learning process. Based on the feedback signals generated during this process (e.g., the language-model likelihood of the output of a model, and the reconstruction error of the original sentence after the primal and dual translations), we can iteratively update the two models until convergence (e.g., using the policy gradient methods). We call the corresponding approach to neural machine translation \emph{dual-NMT}. Experiments show that dual-NMT works very well on English$\leftrightarrow$French translation; especially, by learning from monolingual data (with 10% bilingual data for warm start), it achieves a comparable accuracy to NMT trained from the full bilingual data for the French-to-English translation task.
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[link]
TLDR; The authors finetune an FR -> EN NMT model using a RL-based dual game. 1. Pick a French sentence from a monolingual corpus and translate it to EN. 2. Use an EN language model to get a reward for the translation 3. Translate the translation back into FR using an EN -> FR system. 4. Get a reward based on the consistency between original and reconstructed sentence. Training this architecture using Policy Gradient authors can make efficient use of monolingual data and show that a system trained on only 10% of parallel data and finetuned with monolingual data achieves comparable BLUE scores as a system trained on the full set of parallel data. ### Key Points - Making efficient use of monolingual data to improve NMT systems is a challenge - Two Agent communication game: Agent A only knows language A and agent B only knows language B. A send message through a noisy translation channel, B receives message, checks its correctness, and sends it back through another noisy translation channel. A checks if it is consistent with the original message. Translation channels are then improves based on the feedback. - Pieces required: LanguageModel(A), LanguageModel(B), TranslationModel(A->B), TranslationModel(B->A). Monolingual Data. - Total reward is linear combination of: `r1 = LM(translated_message)`, `r2 = log(P(original_message | translated_message)` - Samples are based on beam search using the average value as the gradient approximation - EN -> FR pretrained on 100% of parallel data: 29.92 to 32.06 BLEU - EN -> FR pretrained on 10% of parallel data: 25.73 to 28.73 BLEU - FR -> EN pretrained on 100% of parallel data: 27.49 to 29.78 BLEU - FR -> EN pretrained on 10% of parallel data: 22.27 to 27.50 BLEU ### Some Notes - I think the idea is very interesting and we'll see a lot related work coming out of this. It would be even more amazing if the architecture was trained from scratch using monolingual data only. Due the the high variance of RL methods this is probably quite hard to do though. - I think the key issue is that the rewards are quite noisy, as is the case with MT in general. Neither the language model nor the BLEU scores gives good feedback for the "correctness" of a translation. - I wonder why there is such a huge jump in BLEU scores for FR->EN on 10% of data, but not for EN->FR on the same amount of data. |
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Anh Nguyen and Jason Yosinski and Yoshua Bengio and Alexey Dosovitskiy and Jeff Clune
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CV
First published: 2016/11/30 (9 years ago)
Abstract: Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. (2016) showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models "Plug and Play Generative Networks". PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw. We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network). Our method also improves the state of the art of Multifaceted Feature Visualization, which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.
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Anh Nguyen and Jason Yosinski and Yoshua Bengio and Alexey Dosovitskiy and Jeff Clune
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CV
First published: 2016/11/30 (9 years ago)
Abstract: Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. (2016) showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models "Plug and Play Generative Networks". PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw. We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network). Our method also improves the state of the art of Multifaceted Feature Visualization, which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.
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[link]
_Objective:_ Find a generative model that avoids usual shortcomings: (i) high-resolution, (ii) variety of images and (iii) matching the dataset diversity. _Dataset:_ [ImageNet](https://www.image-net.org/) ## Inner-workings: The idea is to find an image that maximizes the probability for a given label by using a variant of a Markov Chain Monte Carlo (MCMC) sampler. [](https://cloud.githubusercontent.com/assets/17261080/26675978/3c9e6d94-46c6-11e7-9f67-477c4036a891.png) Where the first term ensures that we stay in the image manifold that we're trying to find and don't just produce adversarial examples and the second term makes sure that find an image corresponding to the label we're looking for. Basically we start with a random image and iteratively find a better image to match the label we're trying to generate. ### MALA-approx: MALA-approx is the MCMC sampler based on the Metropolis-Adjusted Langevin Algorithm that they use in the paper, it is defined iteratively as follow: [](https://cloud.githubusercontent.com/assets/17261080/26675866/bf15cc28-46c5-11e7-9620-659d26f84bf8.png) where: * epsilon1 makes the image more generic. * epsilon2 increases confidence in the chosen class. * epsilon3 adds noise to encourage diversity. ### Image prior: They try several priors for the images: 1. PPGN-x: p(x) is modeled with a Denoising Auto-Encoder (DAE). [](https://cloud.githubusercontent.com/assets/17261080/26678501/1737c64e-46d1-11e7-82a4-7ee0aa8bfe2f.png) 2. DGN-AM: use a latent space to model x with h using a GAN. [](https://cloud.githubusercontent.com/assets/17261080/26678517/2e743194-46d1-11e7-95dc-9bb638128242.png) 3. PPGN-h: incorporates a prior for p(h) using a DAE. [](https://cloud.githubusercontent.com/assets/17261080/26678579/6bd8cb58-46d1-11e7-895d-f9432b7e5e1f.png) 4. Joint PPGN-h: to increases expressivity of G, model h by first modeling x in the DAE. [](https://cloud.githubusercontent.com/assets/17261080/26678622/a7bf2f68-46d1-11e7-9209-98f97e0a218d.png) 5. Noiseless joint PPGN-h: same as previous but without noise. [](https://cloud.githubusercontent.com/assets/17261080/26678655/d5499220-46d1-11e7-93d0-d48a6b6fa1a8.png) ### Conditioning: In the paper they mostly use conditioning on label but captions or pretty much anything can also be used. [](https://cloud.githubusercontent.com/assets/17261080/26679654/6297ab86-46d6-11e7-86fa-f763face01ca.png) ## Architecture: The final architecture using a pretrained classifier network is below. Note that only G and D are trained. [](https://cloud.githubusercontent.com/assets/17261080/26679785/db143520-46d6-11e7-9668-72864f1a8eb1.png) ## Results: Pretty much any base network can be used with minimal training of G and D. It produces very realistic image with a great diversity, see below for examples of 227x227 images with ImageNet. [](https://cloud.githubusercontent.com/assets/17261080/26679884/4494002a-46d7-11e7-882e-c69aff2ddd17.png) |
Salient Region Detection via High-Dimensional Color Transform
Jiwhan Kim and Dongyoon Han and Yu-Wing Tai and Junmo Kim
Conference and Computer Vision and Pattern Recognition - 2014 via Local CrossRef
Keywords:
Jiwhan Kim and Dongyoon Han and Yu-Wing Tai and Junmo Kim
Conference and Computer Vision and Pattern Recognition - 2014 via Local CrossRef
Keywords:
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[link]
**Introduction** * Salient region is an area where a striking combination of features in images is perceived at the first observation. * These features combined to make a region that has a significant distinction with other areas in the image. * This paper presents a map of saliency using linear combination of high-dimensional color representation space. **Related work** * Present work in saliency detection is divided into two groups which are taking into account low-level features, and statistical learning methods. Both approach has variety of results with no clear significance which performs better in saliency detection. * In the first group of taking into account of low-level features, there are approaches on saliency region detection based on color contrast, Markovian approach, and multi-scale saliency based on superpixels that have drawbacks on the pres.ence of high-contrast edges, unpreserved boundary, and segmentation parameters. * Using learning-based methods present are region detection saliency based on the regional descriptor, using graph-based, and sample patches. **Approach** * Paper provides a method of mapping low dimensional color of RGB, CIELab, HSV spaces into high dimensional color. * Method identify the presence of superpixel saliency features using the SLIC method with 500 number of superpixels. * Feature vector is defined by the location of superpixels and then combined with color which proceeds to color space representation computation. * Histogram features are later combined with 8 bins in each of the histogram and distance is computed using the Chi-squared method. * Global and local contrast are used using Euclidean distance with variance parameters of 0.25. * Histogram of gradients method with 31 dimensions is used to extract shape and texture features in the superpixels. As backgrounds tend to have more blur features in the pixel, separation of backgrounds is done using Singular Value Feature which algorithm is following the concept of Eigen images with weight acquired by using Singular Value Decomposition. * 75 dimension of feature vectors are then obtained for saliency detection. These feature maps are combined from all the superpixel operations mentioned. Features included are location features, color features, color histogram features, color contrast features, shape and texture features. * Regression algorithm is then applied to feature vectors. As large databases including 2000 images in a dataset are tested, the best present approach for the algorithm is a random forest. An unlimited number of nodes are applied with 200 trees. * To construct the transformation into high-dimensional color, a Trimap is built by dividing the starting saliency map to three different regions that are 2x2, 3x3, and 4x4. Then seven level of adaptive thresholding is applied to each subregion which then produces 21-level of the locally thresholded map. * Global threshold construct the trimap by the division of local levels, when the levels are more and equal to 18 levels, the map is defined by 1 and when the levels are less and equal than 6 levels, map is defined by 0. https://i.imgur.com/4eF1UZd.png * High dimensional color is used as it combines all the benefits of color representation properties. Nonlinear color representation in RGB and its gradients, CIELab color representation, and saturation and hue in HSV are combined. This produces 11 color channels. * Linear RGB values are not combined as it counteracts with YUV/YIQ color space. * Then gamma correction in the range of 0.5 to 2 with 0.5 intervals is applied, generating 44 high dimensional color vector. * Background that had been separated from the foreground in trimap is then examined to approximate color coefficients' linear combination using the minimum of least square problem of two matrices. The first matrix is a vector with binary value with 0 represents background and 1 represents foreground. The second matrix is color samples multiplied by a coefficient vector. * The map of the saliency region is then built by the summation of color samples applied to the coefficient vector estimation. The whole method is iterated three times to construct a stable saliency map. * The map is then refined by spatial information by adding more weights to pixels that contain foreground region. It is defined by exponential to the power of a parameter 0.5 applied to the minimum Euclidean distance for both foreground and background. It is concluded in the image below: https://i.imgur.com/k9heDiw.png * Algorithm is evaluated using three datasets that contain 5000 images in the MSRA dataset, 1000 images in the ECCSD dataset, and 643 multiple objects in the Interactive co-segmentation dataset. * Eight saliency region detection are compared using precision and recall measurements. The algorithms compared are LC, HC, LR, SF, HS, GMR, and DRFI. * F-measurement to evaluate performance is computed by application of precision-recall, and a quadratic parameter added by one that is divided by the summation of precision, recall, and the quadratic parameter. The quadratic parameter has a value of 0.3. * Algorithm performance placed at the second-best compared to all other methods. **Notes on The Paper** * Paper provides an algorithm that performs well for detecting saliency based on colors by generating features that are high dimensional from low-level features. * In high dimensional color space, salient regions are able to be separated from the background (Rahman, I et al. 2016). * If the algorithm is further developed using classifier, it is then able to integrate richer features as a higher dimension is present (Borji, A et al. 2017). |
