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A framework of keyword based image retrieval using proposed Hog_Sift feature extraction method from Twitter Dataset

机译：一种基于关键字的图像检索框架，该框架使用Twitter数据集中建议的Hog_Sift特征提取方法

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摘要

The huge amount of user generated content about the real world events are generated by social media for every minute. Twitter has gained tremendous popularity for the past few years with millions of tweets for each day. The twitter data can be monitored through the Twitter Streaming API in order to reveal model, benefit and to analyze user behavior. This is the major advantage in this micro-blogging network which is suitable for data mining. In this paper, self-built Twitter dataset are prepared based on event which are reflected in social media and related images of these event are stored in the image database. One goal of this paper is to discover the keyword from the extracted tweets using pre-processing steps. A new proposed HOG_SIFT features are obtained to extract the features of images related to the keyword detection and this is the second goal of our work. The third goal is to retrieve the significant images from the image database based on subspace clustering techniques such as k-subspace and seq-k-subspace algorithms. It is experimentally found that proposed HOG_SIFT feature extraction method is efficient and gives better performance than SIFT method. Similarly clustering algorithms are compared based on the performance measures such as precision, recall and accuracy. It is proved that seq-k-subspace performance better than the k-subspace clustering algorithm.
机译：每分钟，社交媒体都会生成大量有关真实事件的用户生成内容。在过去的几年中，Twitter每天都有上百万条推文，因此获得了极大的欢迎。可以通过Twitter Streaming API监视twitter数据，以揭示模型，受益并分析用户行为。这是适合数据挖掘的微博网络的主要优势。本文基于事件将社交媒体反映出来，构建了自己的Twitter数据集，并将这些事件的相关图像存储在图像数据库中。本文的目标之一是使用预处理步骤从提取的推文中发现关键字。获得了新提出的HOG_SIFT特征，以提取与关键字检测相关的图像特征，这是我们工作的第二个目标。第三个目标是基于子空间聚类技术（例如k子空间和seq-k子空间算法）从图像数据库检索重要图像。实验发现，所提出的HOG_SIFT特征提取方法比SIFT方法有效且具有更好的性能。类似地，基于性能指标（如精度，召回率和准确性）对聚类算法进行比较。证明seq-k-subspace性能优于k-subspace聚类算法。

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