Image Classification using Frequent Itemset Mining
نویسندگان
چکیده
Image classification is one of the most useful and essential research field in computer vision domain and challenging task in the image management and retrieval system. The growing demands for image classification in computer vision having application such as video surveillance, image and video retrieval, web content analysis, biometrics etc. have pushed application developers to search and classify images more efficiently. The main goal of image classification is to classifying image into different classes according to their visual characteristics. In this paper, we propose an approach for image classification by applying frequent itemset mining. Frequent Itemset Mining is used to finding the frequent patterns which is referring as Frequent Local Histograms or FLHs. All Local Histogram information must be maintained for obtaining these FLHs patterns and demonstrate the Bag-of-FLHs based image representation. The main aim of this research is to use PCA-SIFT (Principal Component AnalysisScale Invariant Feature Transform) local descriptor for feature extraction. It is more distinctive, robust to image deformations and more compact than the standard SIFT descriptor. The proposed work reduces the overall time of image classification task and maintaining its overall accuracy.
منابع مشابه
Frequent Itemset Mining Using Rough-Sets
Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cro...
متن کاملResearch on Classification Mining Method of Frequent Itemset
The purpose of association mining is to find the valuable relationships between data sets. The prerequisite of it is to find the frequent itemset first. In view of the existing problems in the present frequent itemset mining, this paper puts forward that data sets should be clustered first, and then the algorithm of frequent itemset mining be applied to every cluster. In this way, algorithm of ...
متن کاملA Survey on Infrequent Weighted Itemset Mining Approaches
Association Rule Mining (ARM) is one of the most popular data mining technique. All existing work is based on frequent itemset. Frequent itemset find application in number of real-life contexts e.g., market basket analysis, medical image processing, biological data analysis. In recent years, the attention of researchers has been focused on infrequent itemset mining. This paper tackles the issue...
متن کاملEffective Use of Frequent Itemset Mining for Image Classification
In this paper we propose a new and effective scheme for applying frequent itemset mining to image classification tasks. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During the construction of the FLHs, we pay special attention to keep all the local histogram information during the mining process and to select the most relevant reduced set of FLH patterns fo...
متن کاملRamp: High Performance Frequent Itemset Mining with Efficient Bit-Vector Projection Technique
Mining frequent itemset using bit-vector representation approach is very efficient for small dense datasets, but highly inefficient for sparse datasets due to lack of any efficient bit-vector projection technique. In this paper we present a novel efficient bit-vector projection technique, for sparse and dense datasets. We also present a new frequent itemset mining algorithm Ramp (Real Algorithm...
متن کامل