2012 Imageclef Webupv Collection

Name: 2012 Imageclef Webupv Collection
Creator: Zenodo
Published: 2018-05-05T06:40:03+08:00

Zenodo2020-09-20 更新2026-05-25 收录

大规模图像标注

多模态特征融合

数据链接：

https://zenodo.org/record/1038532 数据链接链接失效反馈

官方服务：

资源简介：

This document describes the WEBUPV dataset compiled for the ImageCLEF 2012 Scalable image annotation task. The data mentioned here indicates what is ready for download. However, upon request or depending on feedback from the participants, additional data can be released. For debugging purposes, thumbnails of the images in the dataset can be obtained from a web server using '{IID}' the image identifier: http://risenet.prhlt.upv.es/db/img/{IID}.jpg The following is the directory structure of the collection, and bellow there is a brief description of what each compressed file contains. The corresponding MD5 checksums of the files shown (for verifying a correct download) can be found in the md5sums.txt. Directory structure ------------------- . | |--- README.txt |--- md5sums.txt |--- webupv_train_lists.zip |--- webupv_devel_lists.zip |--- webupv_test_lists.zip |--- baseline.zip | |--- feats_textual/ | | | |--- webupv_train_textual.rawfeat.gz | |--- webupv_train_textual.scofeat.gz | |--- webupv_train_textual.keywords.gz | |--- feats_visual/ | |--- webupv_{train|devel|test}_visual_gist.feat.gz |--- webupv_{train|devel|test}_visual_sift_*.feat.gz |--- webupv_{train|devel|test}_visual_csift_*.feat.gz |--- webupv_{train|devel|test}_visual_rgbsift_*.feat.gz |--- webupv_{train|devel|test}_visual_opponentsift_*.feat.gz |--- webupv_{train|devel|test}_visual_colorhist.feat.gz Contents of files ----------------- * webupv_train_lists.zip -> train_iids.txt : IDs of the images in the training set (250000). -> train_rids.txt : IDs of the webpages in the training set. -> train_rimgsrc.txt : The URLs of the images as referenced in each of the webpages. This can also be useful as a textual feature. * webupv_devel_lists.zip -> devel_iids.txt : IDs of the images in the development set (1000). -> devel_concepts.txt : List concepts for the development set. -> devel_gnd.txt : Ground truth concepts for the development set images. * webupv_test_lists.zip -> test_iids.txt : IDs of the images in the test set (2000). -> test_concepts.txt : List concepts for the test set. -> test_gnd.txt : Ground truth concepts for the test set images. * baseline.zip An archive that includes code for computing the evaluation measures for two baseline techniques for the "Scalable concept image annotation" subtask. See the included README.txt for details. * feats_textual/webupv_train_textual.rawfeat.gz The raw text extracted from the webpages near where the images appeared. Each line starts with the image and webpage IDs followed by the text extracted. The position of the image within the text is indicated by the special word '{X}'. The extracted text is somewhat filtered (e.g. there are no HTML tags), although removed words and tags have been replaced by full stops '.' to preserve word distances. The title of the webpage is always included, and it is the first sentence of the text. In total the file has 275749 lines since the images can appear in more than one webpage. * feats_textual/webupv_train_textual.scofeat.gz The processed text extracted from the webpages near where the images appeared. Each line corresponds to one image, having the same order as the train_iids.txt list. The lines start with the image ID, followed by the number of extracted unique words and the corresponding word-score pairs. The scores were derived taking into account 1) the term frequency (TF), 2) the document object model (DOM) attributes, and 3) the word distance to the image. The scores are all integers and for each image the sum of scores is always <=100000 (i.e. it is normalized). * feats_textual/webupv_train_textual.keywords.gz The words used to find the images when querying image search engines. Each line corresponds to an image (in the same order as in train_iids.txt). The lines are composed of triplets: [keyword] [rank] [search_engine] where [keyword] is the word used to find the image, [rank] is the position given to the image in the query, and [search_engine] is a single character indicating in which search engine it was found ('g':google, 'b':bing, 'y':yahoo). * feats_visual/webupv_*.feat.gz The visual features in a simple ASCII text sparse format. The first line of the file indicates the number of vectors (N) and the dimensionality (DIMS). Then each line corresponds to one vector, starting with the number of non-zero elements and followed by pairs of dimension-value, being the first dimension 0. In summary the file format is: N DIMS nz1 Dim(1,1) Val(1,1) ... Dim(1,nz1) Val(1,nz1) nz2 Dim(2,1) Val(2,1) ... Dim(2,nz2) Val(2,nz2) ... nzN Dim(N,1) Val(N,1) ... Dim(N,nzN) Val(N,nzN) The order of the features is the same as in the lists devel_iids.txt, test_iids.txt and train_iids.txt. The procedure to extract the SIFT based features in this subdirectory was conducted as follows. Using the ImageMagick software, the images were first rescaled to having a maximum of 240 pixels, of both width and height, while preserving the original aspect ratio, employing the command: convert {IMGIN}.jpg -resize '240>x240>' {IMGOUT}.jpg Then the SIFT features where extracted using the ColorDescriptor software from Koen van de Sande (http://koen.me/research/colordescriptors). As configuration we used, 'densesampling' detector with default parameters, and a hard assignment codebook using a spatial pyramid as 'pyramid-1x1-2x2'. The number in the file name indicates the size of the codebook. All of the vectors of the spatial pyramid are given in the same line, thus keeping only the first 1/5th of the dimensions would be like not using the spatial pyramid. The codebook was generated using 1.25 million randomly selected features and the k-means algorithm. Contact ------- For further questions, please contact: Mauricio Villegas <mauvilsa@upv.es>

提供机构：

Zenodo

创建时间：

2018-05-05

2012 Imageclef Webupv Collection

资源简介：

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