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Resultados filtrados por Publicador: Universidade Cornell

A Survey on Web Multimedia Mining

Kamde, Pravin M.; Algur, Dr. Siddu. P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/09/2011 Português
Relevância na Pesquisa
49.276357%
Modern developments in digital media technologies has made transmitting and storing large amounts of multi/rich media data (e.g. text, images, music, video and their combination) more feasible and affordable than ever before. However, the state of the art techniques to process, mining and manage those rich media are still in their infancy. Advances developments in multimedia acquisition and storage technology the rapid progress has led to the fast growing incredible amount of data stored in databases. Useful information to users can be revealed if these multimedia files are analyzed. Multimedia mining deals with the extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia files. Also in retrieval, indexing and classification of multimedia data with efficient information fusion of the different modalities is essential for the system's overall performance. The purpose of this paper is to provide a systematic overview of multimedia mining. This article is also represents the issues in the application process component for multimedia mining followed by the multimedia mining models.; Comment: 13 Pages; The International Journal of Multimedia & Its Applications (IJMA) Vol.3...

An Effective Method of Image Retrieval using Image Mining Techniques

Kannan, A.; Mohan, V.; Anbazhagan, N.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/12/2010 Português
Relevância na Pesquisa
48.095195%
The present research scholars are having keen interest in doing their research activities in the area of Data mining all over the world. Especially, [13]Mining Image data is the one of the essential features in this present scenario since image data plays vital role in every aspect of the system such as business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. The other area in the Image mining system is the Content-Based Image Retrieval (CBIR) which performs retrieval based on the similarity defined in terms of extracted features with more objectiveness. The drawback in CBIR is the features of the query image alone are considered. Hence, a new technique called Image retrieval based on optimum clusters is proposed for improving user interaction with image retrieval systems by fully exploiting the similarity information. The index is created by describing the images according to their color characteristics, with compact feature vectors, that represent typical color distributions [12].

Low-rank data modeling via the Minimum Description Length principle

Ramírez, Ignacio; Sapiro, Guillermo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 28/09/2011 Português
Relevância na Pesquisa
47.53293%
Robust low-rank matrix estimation is a topic of increasing interest, with promising applications in a variety of fields, from computer vision to data mining and recommender systems. Recent theoretical results establish the ability of such data models to recover the true underlying low-rank matrix when a large portion of the measured matrix is either missing or arbitrarily corrupted. However, if low rank is not a hypothesis about the true nature of the data, but a device for extracting regularity from it, no current guidelines exist for choosing the rank of the estimated matrix. In this work we address this problem by means of the Minimum Description Length (MDL) principle -- a well established information-theoretic approach to statistical inference -- as a guideline for selecting a model for the data at hand. We demonstrate the practical usefulness of our formal approach with results for complex background extraction in video sequences.

Recent Trends and Research Issues in Video Association Mining

V, Vijayakumar; R, Nedunchezhian
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 09/12/2011 Português
Relevância na Pesquisa
48.10248%
With the ever-growing digital libraries and video databases, it is increasingly important to understand and mine the knowledge from video database automatically. Discovering association rules between items in a large video database plays a considerable role in the video data mining research areas. Based on the research and development in the past years, application of association rule mining is growing in different domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. The purpose of this paper is to provide general framework of mining the association rules from video database. This article is also represents the research issues in video association mining followed by the recent trends.; Comment: 13 pages; 1 Figure; 1 Table

Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

Bari, Nima; Vichr, Roman; Kowsari, Kamran; Berkovich, Simon Y.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/03/2015 Português
Relevância na Pesquisa
38.287466%
Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order to; Comment: IEEE Multimedia Big Data (BigMM 2015)

A language independent web data extraction using vision based page segmentation algorithm

YesuRaju, P; KiranSree, P
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 24/10/2013 Português
Relevância na Pesquisa
37.91087%
Web usage mining is a process of extracting useful information from server logs i.e. users history. Web usage mining is a process of finding out what users are looking for on the internet. Some users might be looking at only textual data, where as some others might be interested in multimedia data. One would retrieve the data by copying it and pasting it to the relevant document. But this is tedious and time consuming as well as difficult when the data to be retrieved is plenty. Extracting structured data from a web page is challenging problem due to complicated structured pages. Earlier they were used web page programming language dependent; the main problem is to analyze the html source code. In earlier they were considered the scripts such as java scripts and cascade styles in the html files. When it makes different for existing solutions to infer the regularity of the structure of the Web Pages only by analyzing the tag structures. To overcome this problem we are using a new algorithm called VIPS algorithm i.e. independent language. This approach primary utilizes the visual features on the webpage to implement web data extraction.; Comment: arXiv admin note: text overlap with arXiv:1201.0385 by other authors without attribution