A mission-oriented citizen science platform for efficient flower classification based on combination of feature descriptors

Recently, we have tackled the problem of unsupervised binary feature selection by proposing a sta- tistical framework based on finite multivariate Bernoulli mixture models which

Our method improves the speed of video feature extraction, feature encoding and ac- tion classification by two orders of magnitude at the cost of minor reduction in recognition

Prototype filtering: The prototype selection is not only based on classification results, it also takes into account the prototype status within the training set in order to

Now that we have introduced our feature-based context-oriented programming approach, in this sec- tion we present the tool we built to help program- mers visualise and animate

We report the performance of (i) a FastText model trained on the training subsed of the data set of 1,000 head- lines, (ii) an EC classification pipeline based on Word2Vec and

According to the proposed approaches in literature, we noted the absence of hybridizations between the BQPSO approach and Clonal Selection Algorithm, in the context of

The features (byte image or n-gram frequency) were used as inputs to the classifier and the output was a set of float values assigned to the nine class nodes.. The node with the

keywords: shape descriptor, leaf image retrieval, plant iden- tification, multiscale triangle representation, local descrip-