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Web Image Clustering

Maha El Choubassi, Ara V. Nefian, Igor Kozintse, Jean-Yves Bouguet, Yi Wu
2007 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  
While image clustering has many important applications ranging from personal to web image management, its use is often limited by the difficulty of extracting reliable semantics from low level image features  ...  The image clusters can be improved by using features extracted from image regions rather than the whole image.  ...  However, the results of these methods for web image clustering are often limited due to the large variations in appearance, position and scale of the objects of interest in web images.  ... 
doi:10.1109/icassp.2007.367296 dblp:conf/icassp/ChoubassiNKBW07 fatcat:thvmiv6tubcq5ervx4qi53pd34

Web Image Semantic Clustering [chapter]

Zhiguo Gong, Leong Hou U, Chan Wa Cheang
2005 Lecture Notes in Computer Science  
Finally, web images are assigned to different clusters based on the similarity between image term vectors and the term vector of the clusters.  ...  This paper provides a novel Web image clustering methodology based on their associated texts. In our approach, the semantics of Web images are firstly represented into vectors of term-weight pairs.  ...  We will discuss how to use this graph for term clustering in following section. Web Image Clustering To cluster Web images, we firstly mine out term clusters using TSN.  ... 
doi:10.1007/11575801_30 fatcat:pdl2334v6zeufjgpcs34teg76q

Web-Scale Image Clustering Revisited

Yannis Avrithis, Yannis Kalantidis, Evangelos Anagnostopoulos, Ioannis Z. Emiris
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
Combined with powerful deep learned representations, we achieve clustering of a 100 million image collection on a single machine in less than one hour.  ...  Large scale duplicate detection, clustering and mining of documents or images has been conventionally treated with seed detection via hashing, followed by seed growing heuristics using fast search.  ...  Another contribution is to revisit web-scale image clustering.  ... 
doi:10.1109/iccv.2015.176 dblp:conf/iccv/AvrithisKAE15 fatcat:xbsfi2ktsnhipb5tl5lwz4g26e

Image Clustering System on WWW using Web Texts
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Wataru Sunayama, Akiko Nagata, Masahiko Yachida
2004 Transactions of the Japanese society for artificial intelligence  
Therefore, this paper proposes an image clustering system that labels images by words related to a search keyword. This relationships are measured by Web pages in WWW.  ...  Although, image labeling is one of the solutions of such a problem, various words are labeled to an image if the words are extracted from only one Web page.  ...  − P r(label) (3) (3) Relation § 3 [AltaVista] AltaVista Image Search (URL)http://www.altavista.com/image/default [Frankel 96] Frankel, C., Swain, M. and Athitsos, V.: Web-Seer:An Image, Search Engine  ... 
doi:10.1527/tjsai.19.580 fatcat:mkac6egzrzepph3lmyjccmagl4

Clustering web images with multi-modal features

Manjeet Rege, Ming Dong, Jing Hua
2007 Proceedings of the 15th international conference on Multimedia - MULTIMEDIA '07  
ABSTRACf Web image clustering has drawn significant. attention in the research community recently. However, not much work has been done in using multi-modal information for clustering Web im8ges.  ...  In this paper, we address the problem of Web image clustering by simultaneous integration of visual and textual features from a graph partitioning perspecth'e. In particular, ....  ...  Consequently, Web image clustering has emerged as an important application. For example, properly grouped Web images can provide a very neat bird's eye vicw of the retrieved images to users.  ... 
doi:10.1145/1291233.1291301 dblp:conf/mm/RegeDH07 fatcat:hdqsbwbx3zaglkqqrtfwv4t77y

Advanced Web Image Retrievel Using Clustering Algorithms

Umesh, Suresha
2011 The International Journal of Multimedia & Its Applications  
In this paper we propose a novel methodology for Web Image retrieval system that takes an image as the input query and retrieves images based on image content.  ...  For segmenting an image, modified k-means clustering algorithm was used to group similar pixel together into K groups with cluster centers.  ...  Wang Research Group for making the low resolution web-crawled image data available for this study.  ... 
doi:10.5121/ijma.2011.3413 fatcat:dkcpfdwugnachmtmevfitfz7py

Web image retrieval reranking with multi-view clustering

Mingmin Chi, Peiwu Zhang, Yingbin Zhao, Rui Feng, Xiangyang Xue
2009 Proceedings of the 18th international conference on World wide web - WWW '09  
In order to address these problems, we propose to use both textual and visual contents of retrieved images to reRank web retrieved results.  ...  In particular, a machine learning technique, a multi-view clustering algorithm is proposed to reorganize the original results provided by the text-based search engine.  ...  Web Image reRanking After grouping the original results based on two-view features, a keyword for each cluster is obtained for reRanking according to the tfidf weight value in a given cluster.  ... 
doi:10.1145/1526709.1526922 dblp:conf/www/ChiZZFX09 fatcat:7ckuogmmivferhenfg3p3wkxay

Web image co-clustering based on tag and image content fusion

Jie Chen, Jianlong Tan, Xiangzhou Yin, Hao Liao
2010 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content  
In order to improve the retrieval performance of web images, in this paper, we propose an error-driven fusion co-clustering algorithm, which combines images' tags, visual contents together for analysis  ...  In Web 2.0 applications, users always label digital images using textual descriptions, which are also called tags. As a result, a web image usually carries both tag and visual content information.  ...  Existing methods primarily perform image clustering by using tag information or the visual features extracted from the web images.  ... 
doi:10.1109/icnidc.2010.5657793 fatcat:2p4kzc6klnfjjfc2tyx2tcngki

Semi-supervised hierarchical clustering for personalized web image organization

Lei Meng, Ah-Hwee Tan
2012 The 2012 International Joint Conference on Neural Networks (IJCNN)  
Existing efforts on web image organization usually transform the task into surrounding text clustering.  ...  Extensive experiments on two real world web image data sets, namely NUS-WIDE and Flickr, demonstrate the effectiveness of our algorithm for large web image data sets.  ...  Thus, our algorithm is suitable for large image data sets.  ... 
doi:10.1109/ijcnn.2012.6252397 dblp:conf/ijcnn/MengT12 fatcat:fpxczfpqz5cb5na64u4cbhmn5y

Shape-based web image clustering for unsupervised object detection?

Wei Zheng, Changhu Wang, Xilin Chen
2011 2011 IEEE International Conference on Multimedia and Expo  
In order to automatically isolate the objects from the Web images for training, only clipart images with simple background are used, which keep most of the shape information of the objects.  ...  A two-stage shape-based clustering algorithm is proposed to mine typical shapes of the object, in which the inner-class variance of object shapes is considered and undesired images are filtered out.  ...  This kind of work might not work well on the web images since it is hard to specify this number for web images with countless objects.  ... 
doi:10.1109/icme.2011.6011863 dblp:conf/icmcs/ZhengWC11 fatcat:w233plyul5covajwanaihyn7c4

Annotating Web Image using Parallel Graph Bipartition and Word Clustering

Zheng Liu
2010 Journal of Computers  
A novel web image annotation method by candidate annotations clustering and parallel graph bipartition is proposed in this paper.  ...  For Web images, the candidate annotation sets of which are usually fairly large. Therefore, we cluster candidate annotations to reduce computation complexity.  ...  Graph-based Web image annotation algorithm Input: An image I with t candidate annotations which is denoted as 1 2 { , , } t A A A ……, Candidate annotations clustering: Using K-means to cluster the  ... 
doi:10.4304/jcp.5.8.1185-1192 fatcat:73jdsdfhyzaxvb4g76qoa6536a

Text Steganography in Statistically Clustered Iris Image

Irtefaa Neamah, Hind Mohammed
2018 EAI Endorsed Transactions on Energy Web  
Then, the random variables' data were included in the iris segment, cut-off, and iris' clustered image.  ...  It is impossible to distinguish between the iris image before and after concealment, and the difference between the two images only after using statistical measures such as PSNR and MSR to compare them  ...  EAI Endorsed Transactions on Energy Web Statistical Cluster Analysis Clustering is an unattended automated learning method that means there is no information about the output.  ... 
doi:10.4108/eai.18-11-2020.167100 fatcat:zp4iywkbsvatdn7qttch3rklna

Clustering web images using association rules, interestingness measures, and hypergraph partitions

Hassan H. Malik, John R. Kender
2006 Proceedings of the 6th international conference on Web engineering - ICWE '06  
This paper presents a new approach to cluster web images. Images are first processed to extract signal features such as color in HSV format and quantized orientation.  ...  Online steps are done in real-time, which makes this approach practical for web images.  ...  RELATED WORK 2.1 Web Image Clustering There have been several web image clustering and categorization approaches proposed in recent years. We discuss only a few representative approaches here.  ... 
doi:10.1145/1145581.1145591 dblp:conf/icwe/MalikK06 fatcat:7j66wracvrepfefcprmnona7zq

Graph theoretical framework for simultaneously integrating visual and textual features for efficient web image clustering

Manjeet Rege, Ming Dong, Jing Hua
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
Consequently, Web image clustering has emerged as an important application.  ...  However, not much work has been done in using multimodal information for clustering Web images.  ...  Consequently, Web image clustering has drawn significant attention in the research community recently.  ... 
doi:10.1145/1367497.1367541 dblp:conf/www/RegeDH08 fatcat:to37qv553rfyrdvmsqfczbpnym

Segmentation of Brain Images by Optimizing Clustering of Convolution Based Features

Vikas Kumar, S. Krit
2021 E3S Web of Conferences  
(MRI) images.  ...  Magnetic Resonance Imaging based brain tumour segmentation studies are attracting more and more attention in recent years thanks to non-invasive imaging and good soft tissue contrast of resonance Imaging  ...  /doi.org/10.1051/e3sconf/202122901034 ICCSRE'2020 E3S Web of Conferences 229, 01034 (2021) https://doi.org/10.1051/e3sconf/202122901034 ICCSRE'2020 E3S Web of Conferences 229, 01034 (2021) https  ... 
doi:10.1051/e3sconf/202122901034 fatcat:sbcfljc2ojezdhfoc7mjagpen4
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