Feature Selection in Big Image Datasets

In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level features. However, the resulting datasets have grown in the number of features, leading into long training times due to the curse of dimensionality. In this research, some feature selection methods were applied to these image features through big data technologies. Additionally, we analyzed how image resolutions may affect to extracted features and the impact of applying a selection of the most relevant features. Experimental results show that making an important reduction of the extracted features provides classification results similar to those obtained with the full set of features and, in some cases, outperforms the results achieved using broad feature vectors.