Visual modality: Color, shape, size, motion, texture (raw); patterns, symmetry, depth cues (derived)
Auditory modality: pitch, volume, tempo, rhythm (raw); speech patterns, tone of voice (derived)
haptics ? emotions ? language ?
Numerical: Continuous or discrete values (e.g., height, number of words).
Categorical: Representing distinct groups (e.g., color, category labels).
Derived: Transformed or engineered values combining raw data (e.g., ratios, log values).
Raw Features: Pixel intensity values, RGB color values.
Engineered Features: Haar features, Gabor wavelets, Histogram of gradients (HOG), edge counts, convolutional feature maps.
Context: In object detection, pixel patterns or edge-based features help detect objects in the image.
Raw Features: occurence of specific character sequences, word or token counts, sequence length
Engineered Features: Word "embeddings" (e.g., Word2Vec, BERT embeddings),
Context: In sentiment analysis, embeddings provide dense, meaningful representations of text features.
Raw Features: Waveform amplitudes, signal energy.
Engineered Features: Mel-frequency cepstral coefficients (MFCCs), spectrogram data, pitch.
Context: In speech recognition, MFCCs are features extracted to characterize the audio signal.