Pruning SIFT & SURF for Efficient Clustering of Near-duplicate Images

Adaptive bag‐of‐visual word modelling using stacked‐autoencoder and particle swarm optimisation for the unsupervised categorisation of images

“…In [8] the authors demonstrated that the opportunity to change the number of layers and the number of neurons in each layer of a Deep Learning algorithm allows the feature extraction process to be adaptable to the content diversity of the image collection during BOVW modelling, thus generating image feature vectors whose dimension guarantees optimum discrimination, unlike the fixed 128 dimensions of Scale Invariant Feature Transform (SIFT) and 64 dimensions of Speeded-Up Robust Feature (SURF) [57,58,59].…”