Thesis Replication and Algorithm Discussion Learning

This week I came across a very well created paper on semantic segmentation for image processing tasks, Semantic Soft Segmentation, which was published in ACM trans. Graphs., Vol 37 No. 4 of 72, from several teachers at MS , MITCSAIL and Adobe Research Institute. I myself am new to some exploration and learning in this area of image processing, but of course the way to get into and know a field quickly is to learn from the best players in the field. I think that's what this article is all about, plus I really hope I can get to the heart of this article, which, in my opinion, offers a lot of new insights and possibilities.

First of all, some technical terms are introduced, Semantic Soft Segments (SSS), which aims to accurately represent the soft transitions between different regions of an image. Similar to the magnetic lasso and magic wand functions.

This is where the central task of the article lies.

Next, then we slowly read the paper, and I think good algorithms and understanding are born out of the replication of the paper.

First you can enjoy the video of the effect given by the author.

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Now we can understand the article from the beginning.

## Abstarct section

For article reading, learning and understanding its context is always primary, and in the midst of learning the context, it is very meaningful to clarify its purpose.

As stated from the introduction of the matching image in Figure 1 up, we have proposed a method that enables semantic soft segmentation.

We propose a method that can generate soft segments, i.e. layers that represent the semantically meaningful regions as well as the soft transitions between them, automatically by fusing high-level and low-level image features in a single graph structure. The semantic soft segments, visualized by assigning each segment a solid color, can be used as masks for targeted image editing tasks, or selected layers can be used for compositing after layer color estimation. Original images are from [Lin et al. 2014] (top-left, bottom-right), by Death to the Stock Photo (top-right) and by Y. Aksoy (bottom-left).

We propose a method that generates soft splits. The word semantic in English refers to the lexical units that make up the corpus of language, and is used over here as a characterization of the independent connected units that occur in a picture. For an understanding of independently linked cells, see some basic concepts of topological spaces. In image cognition theory, graphology and geometry are intrinsically linked in a meaningful way. How to implement the division of independent connected units in an image using computer is an important computer task and the main scenario involved in it contains, image restructuring techniques, image fusion techniques, image secondary processing and other techniques. To facilitate better processing of images by computers, a separate series of research efforts have been undertaken on this task.

Layer theory forms the basic idea of image processing. So, in the second sentence the author explains the concept of layers, layers that represent the semantically meaningful regions. regions that can represent special meanings in an image are separated out and thus constitute layers. The computer semantic segmentation task can then be further understood as the task of efficiently extracting the layers of which the image is composed.

The concept of soft transitions is proposed later with as well as following the previous layers, thus essentially suggesting that the core purpose of this paper is how to use their proposed method (they proposed to solve the problem of soft transitions between layers of purpose, simply speaking, in image imaging, there is no such concept as layers, which is one of the processing techniques in the field of image processing. The imaging under natural conditions is reached by natural light through reflection, and there is no concept of layer segregation in different semantic areas, and the transition between them is full and true, without abruptness or sharpness. And when the digital image storage technology is implemented, the semantic regions on the image are represented by pixel values, i.e., the image has detailed partitions and there is no theoretical continuous transition. This has led to the development of interpolation algorithms such as sharpening, feathering, and filtering at the image processing field, thus satisfying a reasonable transition in the semantic region of the image. Further, how to implement the reverse structural semantic layer? the authors of this paper propose automatic fusion (fusing)

High-level and low-level image features approach to complete deconstruction and semantic reshaping against digital images.

The meaning of the word fusing indicates that the strategy used by the author is mainly fusion. fusing itself refers to the melting of metals, etc., meaning fusing into each other and becoming one. Here it is understood that the layers are related to each other and support each other to complete the construction. Later the authors go on to give the characteristics and main applications of semantic soft segmentation, i.e., tools that are displayed by a single color in an image, that can be used in image editing tasks darkness can be used as a mask to process the image, or as a means of target semantic region selection to provide reliable judgments for synthetic images.

## End of background

Abstract Text

Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introducesemantic soft segments, a set of layers that correspond to semantically meaningful regions in an image with accurate soft transitions between different objects. We approach this problem from a spectral segmentation angle and propose a graph structure that embeds texture and color features from the image as well as higher-level semantic information generated by a neural network. The soft segments are generated via eigendecomposition of the carefully constructed Laplacian matrix fullyautomatically. We demonstrate that otherwise complex image editing tasks can be done with little effort using semantic soft segments.

CCS concepts:•Computing methodologies→Image segmentation;

Additional Key Words and Phrases: soft segmentation, semantic segmentation, natural image matting, image editing, spectral segmentation

Paraphrasing and understanding of the abstract section. It is very important to start or open the paper, which is very important for writing the paper, as it is said that the name of the teacher can be right. Accurate representation of soft transitions between image semantics is of essential importance in the field of high quality image processing and and synthesis. This is followed immediately by a description of the dilemma and the current situation faced by the field of image processing in response to this problem, thus providing a good representation of the perfect alignment of the author's work with real world needs. (Can't help but admire the author for being such a good writer~) For professional image processors this is a tedious task dull and repetitive. Immediately after, in this work, simple and clear into the main idea, we introduced (developed), the author is very humble, using the word introduce, rather than develop or create, etc., thus well indicate that their work is a further expansion on the basis of their predecessors, rather than creative pioneering work.

Immediately following the full description of the whole method, the authors introduce semantic soft segments , and the reason for using segments instead of segmentation indicates that here is a concrete way of segmentation, rather than the abstract concept of segmentation referred to by segmation. The composition of this method, a set of layers, and what it is, i.e., a collection of layers with meaningful regions of accurately partitioned transitions, is elaborated immediately afterwards. Taking this a step further, we approach the problem by combining the perspective of spectral segmantation with a picture (graph) structure that combines image texture and color features, and high-dimensional semantic information composed through a neural network.

where soft segments are automatically implemented by carefully constructing a Laplace matrix and solving for its eigenvalues.

Finally, the authors present our demonstration of the ease with which complex image editing tasks can be achieved through soft segments.

Computer concepts： Computer Methodology--> Image segmentation（image segmentation）

Additional keywords: soft segmentation; semantic segmentation natural image matting; image editing; spectral segmentation.

## End of summary section.

## Introduction section

The introduction section should still be read as a combing of the literature and the field, and the authors spend no less effort in the introduction section than in the method section. Of course the INTRODUCTION section will also hint at the core of the paper and the main conclusions and possible areas of relevance.

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