Sentence examples for feature quantization from inspiring English sources

Many previous works on visual classification have relied on bag-of-words model, BoW [11], local feature quantization and SVM.

However, information loss in feature quantization is still inevitable and affects the performance for good image classification performance.

Of course, this type of feature quantization introduces the respective loss of the discriminative ability of the features.

The information loss in the feature quantization through pooling is still found, though several coding methods are introduced to address this problem.

After the feature quantization step, the image is reduced as a set of patches taken from a fixed size patch vocabulary that can be encoded as a patch histogram according to (4).

It involves extraction of local image features, quantization of the feature space into a codebook through clustering, and extraction of codeword distribution histograms.

In contrast, the GMM approach is more accurate, as it allows good likelihood ratio estimates while providing a finer feature space quantization through the soft assignment possibility.

"A few key ingredients, namely feature pruning, quantization, hashing, and re-training, allow us to produce text classification models with tiny size, often less than 100kB when trained on several popular datasets, without noticeably sacrificing accuracy or speed," said the Facebook authors of a December 2016 paper entitled "fastText.zip: Compressing Text Classification Models".

Another set of features were extracted from those features by applying quantization on the histogram of the images.

Both data sets show that by increasing the number of features at a fixed -bit quantization per feature pair, the performances of LS + APQ improves and becomes stable.

LPQ features: Local phase quantization (LPQ) [44] operator uses local phase information to produce blur-invariant image features extracted by computing the short-term Fourier transform (STFT) in rectangular neighborhoods N x : F u,x =sum_{yin N_{x}}f x-y e^{-j2rho u^{T}y} = mathbf{W}^{T}_{u} mathbf{f}_{x} (4).