{"title":"基于内容的基于CBIR和ABIR算法的视频图像检索","authors":"V. A. Wankhede, P. Mohod","doi":"10.1109/GCCT.2015.7342767","DOIUrl":null,"url":null,"abstract":"Content-based video retrieval is very interesting point where it can be used in our daily life. Video retrieval is regarded as one of the most important in multimedia research. The development of multimedia data types there is demand of video retrieval system. Video retrieval can be used for video search and browsing which are useful in web applications. Selection of extracted features play an important role in content based video retrieval. There are two types of feature extraction, low level feature extraction and high level feature extraction. Low level feature extraction based on color, shape, texture, spatial relationship. The main goal of this paper is that, user can give the two different types of input in the form of image query and the text query. First one is that give the input in the form of image query and retrieved image which is similar to the query image by using the CBIR algorithm. CBIR is still developing science. Retrieval of images based on visual features such as color, texture and shape. In this paper gives a detail description of a system developed for retrieving images similar to a query image from a different large set of image. Second one is that give the input in the form of text query and retrieved image by using the ABIR technique. ABIR is powerful algorithm for the information retrieval it can utilize their powerful natural language. Annotation is never complete.","PeriodicalId":378174,"journal":{"name":"2015 Global Conference on Communication Technologies (GCCT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Content-based image retrieval from videos using CBIR and ABIR algorithm\",\"authors\":\"V. A. Wankhede, P. Mohod\",\"doi\":\"10.1109/GCCT.2015.7342767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Content-based video retrieval is very interesting point where it can be used in our daily life. Video retrieval is regarded as one of the most important in multimedia research. The development of multimedia data types there is demand of video retrieval system. Video retrieval can be used for video search and browsing which are useful in web applications. Selection of extracted features play an important role in content based video retrieval. There are two types of feature extraction, low level feature extraction and high level feature extraction. Low level feature extraction based on color, shape, texture, spatial relationship. The main goal of this paper is that, user can give the two different types of input in the form of image query and the text query. First one is that give the input in the form of image query and retrieved image which is similar to the query image by using the CBIR algorithm. CBIR is still developing science. Retrieval of images based on visual features such as color, texture and shape. In this paper gives a detail description of a system developed for retrieving images similar to a query image from a different large set of image. Second one is that give the input in the form of text query and retrieved image by using the ABIR technique. ABIR is powerful algorithm for the information retrieval it can utilize their powerful natural language. Annotation is never complete.\",\"PeriodicalId\":378174,\"journal\":{\"name\":\"2015 Global Conference on Communication Technologies (GCCT)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Global Conference on Communication Technologies (GCCT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCT.2015.7342767\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Global Conference on Communication Technologies (GCCT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCT.2015.7342767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Content-based image retrieval from videos using CBIR and ABIR algorithm
Content-based video retrieval is very interesting point where it can be used in our daily life. Video retrieval is regarded as one of the most important in multimedia research. The development of multimedia data types there is demand of video retrieval system. Video retrieval can be used for video search and browsing which are useful in web applications. Selection of extracted features play an important role in content based video retrieval. There are two types of feature extraction, low level feature extraction and high level feature extraction. Low level feature extraction based on color, shape, texture, spatial relationship. The main goal of this paper is that, user can give the two different types of input in the form of image query and the text query. First one is that give the input in the form of image query and retrieved image which is similar to the query image by using the CBIR algorithm. CBIR is still developing science. Retrieval of images based on visual features such as color, texture and shape. In this paper gives a detail description of a system developed for retrieving images similar to a query image from a different large set of image. Second one is that give the input in the form of text query and retrieved image by using the ABIR technique. ABIR is powerful algorithm for the information retrieval it can utilize their powerful natural language. Annotation is never complete.