A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image Retrieval

Big Data and Cognitive Computing

Ghalem Belalem¹  Aurélie Cools²  Sidi Ahmed Mahmoudi²  Mohammed Amin Belarbi²  Saïd Mahmoudi² 

[1] Department of Computer Science, Faculty of Exact and Applied Sciences, University of Oran1 Ahmed Ben Bella, Oran 31000, Algeria;Faculty of Engineering, University of Mons, 7000 Mons, Belgium;

关键词: indexing image;    VA-File;    PCA;    LSH;    binary tree;    SIFT;   

DOI  :  10.3390/bdcc6020054

来源: DOAJ

【 摘 要 】

Indexing images by content is one of the most used computer vision methods, where various techniques are used to extract visual characteristics from images. The deluge of data surrounding us, due the high use of social and diverse media acquisition systems, has created a major challenge for classical multimedia processing systems. This problem is referred to as the ‘curse of dimensionality’. In the literature, several methods have been used to decrease the high dimension of features, including principal component analysis (PCA) and locality sensitive hashing (LSH). Some methods, such as VA-File or binary tree, can be used to accelerate the search phase. In this paper, we propose an efficient approach that exploits three particular methods, those being PCA and LSH for dimensionality reduction, and the VA-File method to accelerate the search phase. This combined approach is fast and can be used for high dimensionality features. Indeed, our method consists of three phases: (1) image indexing within SIFT and SURF algorithms, (2) compressing the data using LSH and PCA, and (3) finally launching the image retrieval process, which is accelerated by using a VA-File approach.

【 授权许可】

Unknown