Proceedings ArticleDOI
Pruning SIFT & SURF for Efficient Clustering of Near-duplicate Images
Tushar Shankar Shinde,Anil Kumar Tiwari +1 more
- pp 3132-3136
TLDR
A simple approach to reduce the cardinality of keypoint set and prune the dimension of SIFT and SURF feature descriptors for efficient image clustering is proposed, and clustering accuracy is found to be at par with traditional SIFTand SURF with a significant reduction in computational cost.Abstract:
Clustering and categorization of similar images using SIFT and SURF require a high computational cost. In this paper, a simple approach to reduce the cardinality of keypoint set and prune the dimension of SIFT and SURF feature descriptors for efficient image clustering is proposed. For this purpose, sparsely spaced (uniformly distributed) important keypoints are chosen. In addition, multiple reduced dimensional variants of SIFT and SURF descriptors are presented. Moreover, clustering time complexity is also improved by proposed contextual bag-of-features approach for partitioned keypoint set. The F-measure statistic is used to evaluate clustering performance on a California-ND dataset containing near-duplicate images. Clustering accuracy of the proposed pruned SIFT and SURF is found to be at par with traditional SIFT and SURF with a significant reduction in computational cost.read more
Citations
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Proceedings ArticleDOI
Efficient Image Set Compression
TL;DR: This thesis proposes approaches for efficient clustering, fast direction oriented motion estimation algorithm, and an image reordering scheme with minimum predictive costs for better compression of near-duplicate image collection.
Proceedings ArticleDOI
Local Image Feature Extraction using Stacked-Autoencoder in the Bag-of-Visual Word modelling of Images
Abass A. Olaode,Golshah Naghdy +1 more
TL;DR: The proposed Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning via Stacked-Autoencoder is tested and the ability of deep feature learning to yield optimum image categorisation performance is confirmed.
Journal ArticleDOI
Adaptive bag-of-visual word modelling using stacked-autoencoder and particle swarm optimisation for the unsupervised categorisation of images
Abass A. Olaode,Golshah Naghdy +1 more
TL;DR: This study presents an adaptive BOVW modelling, in which image feature extraction is achieved using deep feature learning and the amount of computation required for the development of visual codebook is minimised using a batch implementation of particle swarm optimisation.
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