MapReduce Based Remote Sensing Image Retrieval Algorithm

Shen Xibing,Wei Rong,Yang Yi 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.8

The remote sensing images are massively stored, so it is difficult for the traditional single-node mode to meet the real-time requirement for remote sensing image retrieval. In order to improve remote sensing image retrieval efficiency and accuracy, a kind of feature information MapReduce based remote sensing image retrieval algorithm is proposed in this article. Specifically, the color features and the texture features of the remote sensing image are firstly extracted, and then Map function is adopted to calculate the similarity between the remote sensing image to be retrieved and the image in the feature library according to the color features and the texture features, and finally Reduce function is adopted to collect the intermediate results of various node tasks and the remote sensing images are ranked by a descending order according to the similarity in order to obtain the remote sensing image retrieval result. The test result shows that the proposed algorithm can rapidly and accurately retrieve the remote sensing image, thus not only improving the remote sensing image retrieval efficiency, but also improving the remote sensing image retrieval accuracy.

Image Retrieval Process Based on Relevance Feedback and Ontology Using Decision Tree

Debnath Bhattacharyya,Dipankar Hazra,Tai-hoon Kim 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.10

In this paper, another strategy for immediate features based image recovery is proposed. Image database is developed with low level texture features got from Gray Level Co- Occurrence Matrix (GLCM) and measurable techniques for Tamura. Semantic level inquiries from the user mapped to the low level peculiarities at recovery time to recover the required images. Images with more than one moderate features can be recovered by utilizing intersection of images recovered by each of the queried feature. Artificial Neural Network (ANN) is utilized as a part of the following steps in the wake of accepting user inputs. In spite of the fact that semantics are utilized as search key as a part of the beginning steps, low level features are utilized as a part of the ANN based searching in later steps. Back propagation Algorithm is utilized as a part of learning step. This ANN based relevance feedback technique enhances accuracy of immediate feature based image retrieval method. Decision tree (DT) can likewise be connected in relevance feedback stage. Decision tree is framed in training stage and images will be tested by of the decision tree. Relation storing ontology related information is utilized as a part of every phase of retrieval procedure to evacuate ambiguities identified with synonyms and hypernym-homonym sets.

적합성 피드백을 통해 결정된 가중치를 갖는 시각적 특성에 기반을 둔 이미지 검색 모델

송지영(Ji-Young Song),김우철(Woo-Cheol Kim),김승우(Seung-Woo Kim),박상현(Sanghyun Park) 한국정보과학회 2007 정보과학회논문지 : 데이타베이스 Vol.34 No.3

디지털 이미지의 양이 증가함에 따라 원하는 이미지를 정확하고 빠르게 찾을 수 있는 방법의 필요성이 증가하고 있다. 이미지 검색 방법으로는 이미지의 색상이나 명암과 같은 시각적 특성을 검색 조건으로 이용하는 내용 기반 검색과 이미지를 설명하는 키워드를 검색 조건으로 이용하는 키워드 기반 검색이 있다. 하지만 이러한 방법만으로는 사용자가 원하는 이미지를 정확하게 찾기 힘들다는 문제점이 제기되어 왔다. 따라서 최근에는 검색 도중 사용자의 응답을 받아 사용자의 요구를 파악함으로써 향상된 검색결과를 제공하는 적합성 피드백에 대한 연구가 많이 진행되고 있다. 하지만 적합성 피드백을 이용하는 방법들도 원하는 결과를 얻기 위해서는 여러 번의 피드백을 필요로 하고 질의 수행이 완료된 후에는 얻어진 피드백 정보를 재사용하지 못한다는 단점이 있다. 따라서 본 논문에서는 이미지에 키워드를 연결한 후 사용자의 피드백 정보를 반영하여 키워드의 신뢰도를 조절함으로써 키워드 기반 이미지 검색의 정확도를 높일 수 있는 모델을 제안한다. 제안된 모델에서는 사용자로부터 피드백을 받은 이미지뿐만 아니라 긍정적 피드백을 받은 이미지들이 공통적으로 가지는 시각적 특성과 유사한 시각적 특성을 가지는 다른 이미지들까지도 키워드의 신뢰도를 조정함으로써 좀 더 빠른 시간 내에 검색 결과의 정확도를 높이도록 한다. 제안한 방법의 정확성을 검증하기 위한 실험 결과에 따르면, 같은 횟수의 피드백을 받으면서도 재현율과 정확률은 빠른 증가를 보이는 것으로 나타났다. Increasing amount of digital images requires more accurate and faster way of image retrieval. So far, image retrieval method includes content-based retrieval and keyword based retrieval, the former utilizing visual features such as color and brightness and the latter utilizing keywords which describe the image. However, the effectiveness of these methods as to providing the exact images the user wanted has been under question. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as a feedback during the retrieval session in order to define user’s need and provide improved result. Yet, the methods which have employed relevance feedback also have drawbacks since several feedbacks are necessary to have appropriate result and the feedback information can not be reused. In this paper, a novel retrieval model has been proposed which annotates an image with a keyword and modifies the confidence level of the keyword in response to the user’s feedback. In the proposed model, not only the images which have received positive feedback but also the other images with the visual features similar to the features used to distinguish the positive image are subjected to confidence modification. This enables modifying large amount of images with only a few feedbacks ultimately leading to faster and more accurate retrieval result. An experiment has been performed to verify the effectiveness of the proposed model and the result has demonstrated rapid increase in recall and precision while receiving the same number of feedbacks.

Text-based Image Indexing and Retrieval using Formal Concept Analysis

( Imran Shafiq Ahmad ) 한국인터넷정보학회 2008 KSII Transactions on Internet and Information Syst Vol.2 No.3

In recent years, main focus of research on image retrieval techniques is on content-based image retrieval. Text-based image retrieval schemes, on the other hand, provide semantic support and efficient retrieval of matching images. In this paper, based on Formal Concept Analysis (FCA), we propose a new image indexing and retrieval technique. The proposed scheme uses keywords and textual annotations and provides semantic support with fast retrieval of images. Retrieval efficiency in this scheme is independent of the number of images in the database and depends only on the number of attributes. This scheme provides dynamic support for addition of new images in the database and can be adopted to find images with any number of matching attributes.

시맨틱 갭을 줄이기 위한 딥러닝과 행위 온톨로지의 결합 기반 이미지 검색

이승,정혜욱 사단법인 인문사회과학기술융합학회 2019 예술인문사회융합멀티미디어논문지 Vol.9 No.11

Recently, the amount of image on the Internet has rapidly increased, due to the advancement of smart devices and various approaches to effective image retrieval have been researched under these situation. Existing image retrieval methods simply detect the objects in a image and carry out image retrieval based on the label of each object. Therefore, the semantic gap occurs between the image desired by a user and the image obtained from the retrieval result. To reduce the semantic gap in image retrievals, we connect the module for multiple objects classification based on deep learning with the module for human behavior classification. And we combine the connected modules with a behavior ontology. That is to say, we propose an image retrieval system considering the relationship between objects by using the combination of deep learning and behavior ontology. We analyzed the experiment results using walking and running data to take into account dynamic behaviors in images. The proposed method can be extended to the study of automatic annotation generation of images that can improve the accuracy of image retrieval results. 최근 스마트 기기의 발전으로 인터넷상에 존재하는 이미지 데이터의 양이 급속하게 증가하는 상황에서 효과적인 이미지 검색을 위한 다양한 방법들이 연구되고 있다. 기존의 이미지 검색 방법들은 이미지에 존재하는 물체들을 단순하게 검출하여 각 물체들의 라벨 정보에 근거한 검색을 수행하기 때문에 사용자가 원하는 이미지와 검색 결과로 얻은 이미지 간에 의미적 차이인 시맨틱 갭(Semantic Gap)이 발생된다. 이미지 검색에서 발생하는 시맨틱 갭을 줄이기 위해, 본 논문에서는 딥러닝 기반의 다중 객체 분류 모듈과 사람의 행위를 분류하는 모듈을 연결하고, 이 모듈들에 행위 온톨로지를 결합하였다. 즉, 딥러닝과 행위 온톨로지의 결합을 기반으로 객체들 간의 연관성을 고려한 이미지 검색 시스템을 제안한다. 이미지에 포함된 동적인 행위를 고려하기 위해 Walking과 Running 데이터를 이용하여 실험한 결과를 분석하였다. 제안한 방법은 향후 이미지 검색 결과의 정확도를 높일 수 있는 영상의 자동 주석 생성 연구에 확장하여 적용할 수 있다.

DETR 기반의 객체 임베딩을 활용한 이미지 검색

반충기,박다영,황영배,이용,장래영,최명석 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 논문지 Vol.18 No.5

Image retrieval is a problem of finding an image suitable for the purpose of search for a given image. Recent work utilizes a feature map of pre-trained deep learning models from large dataset for image classification to solve the content-based image retrival (CBIR) problem, mainly finding visually similar images. This paper proposes an object-based image retrival (OBIR) problem of finding an image containing an object same as a given image. Traditional image retrieval techniques use global or local features to search for similar images, which makes it difficult to obtain object information if a given image has a complex background or if the object of the image is small. To address this, we propose object embedding and image retrieval techniques using the latest object recognition models, DETR (Detection Transformer) and Bag of Visual Words (BoVW), which can represent object information regardless of background. This paper conduct a comparative experiment with existing image retrieval techniques using COCO data mainly used in object recognition problems and AID, Oxford & Paris data used in existing image retrieval techniques, and show that DETR-based BoVW outperforms existing methods in OBIR problems.

Research on New Multi-Feature Large-Scale Image Retrieval Algorithm based on Semantic Parsing and Modified Kernel Clustering Method

Tiejun Wang,Weilan Wang 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.1

Because of the feature points can describe the local characteristics of the image in a reasonable manner, effective use of feature point of content based image retrieval become the current hot issues in the field of computer vision. Aiming at this problem, we put forward a kind of combination clustering based on feature points, a new method of image retrieval. The method includes the combination of feature point clustering algorithm and based on the algorithm of local color histogram construction strategy. With the existing and local color histogram retrieval method based on feature points, compared to the method can effectively solve the current method of feature point location information and feature point center relying too much on the problem. Subjectivity and as a result of the manual annotation image accuracy, the traditional image retrieval methods cannot meet the needs of the user. Multidimensional indexing technology is only from the perspective of how to improve the indexing algorithm to adapt to the large-scale database to consider a problem, in content-based image retrieval. Our research combines the advantages of the semantic analysis and kernel clustering which will enhance the performance of the traditional image retrieval methods and strengthen the feasibility of the algorithm.

Research on Image Retrieval Technology Based on Fast Wavelet Transform

보안공학연구지원센터(IJHIT) 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.4

In present research, the image feature based on adaptive wavelet has been widely used in the content-based image retrieval field. However, there is a common problem in these methods, which is to describe different query images with the same wavelet basis. In order to improve the adaptability of the image retrieval technology, we design different image basis for different query images, to achieve characterizing the feature-changing of different image categories with the adjustable distance measure. We also use the approximate Taylor expansion to reduce the seeking time of the characterization image and the characterization derivative image. As the experimental results have shown that, the new image retrieval technology with high adaptability can improve the retrieval performance greatly.

모바일 환경에서 의미 기반 이미지 어노테이션 및 검색

노현덕,서광원,임동혁 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.8

The progress of mobile computing technology is bringing a large amount of multimedia contents such as image. Thus, we need an image retrieval system which searches semantically relevant image. In this paper, we propose a semantic image annotation and retrieval in mobile environments. Previous mobile-based annotation approaches cannot fully express the semantics of image due to the limitation of current form (i.e., keyword tagging). Our approach allows mobile devices to annotate the image automatically using the context-aware information such as temporal and spatial data. In addition, since we annotate the image using RDF(Resource Description Framework) model, we are able to query SPARQL for semantic image retrieval. Our system implemented in android environment shows that it can more fully represent the semantics of image and retrieve the images semantically comparing with other image annotation systems

Document Image Retrieval: Algorithms, Analysis and Promising Directions

Mohammadreza Keyvanpour,Reza Tavoli 보안공학연구지원센터 2013 International Journal of Software Engineering and Vol.7 No.1