Abstract:
A method detects objects in a scene over time. Sets of time-aligned features are extracted from multiple signals representing a scene over time; each signal is acquired using a different modality. Each set of time-aligned features is arranged as a vector in a matrix to which a first transform is applied to produce a compressed matrix. A second transform is applied to the compressed matrix to extract spatio-temporal profiles of objects occurring in the scene.

Description:
FIELD OF THE INVENTION  
       [0001]     The invention relates generally to the field of signal processing and in particular to detecting temporally related components of signals of different input modalities.  
       BACKGROUND OF THE INVENTION  
       [0002]     We perceive the real world by integrating cues from multiple modalities of perception. Our mental representation is not just based on what we see, but also on the sounds we hear, what we smell, as well as other sensory inputs. For example, fireworks are perceived and remembered as bright flashes of lights, soon followed by loud explosions, concussion waves, and the smell of gun powder.  
         [0003]     In contrast, conventional computer recognition systems only operate on signals acquired in a single input modality, e.g., either visual signals, or alternatively, audio signals. Patterns recognized separately in different domain are sometimes combined heuristically after processing. That presumes a prior understanding of how different signals are related, see Hershey et al., in “ Using audio - visual synchrony to locate sounds ,” Advances in Neural Information Processing Systems 12. MIT Press, Cambridge Mass. 1999, Slaney et al., in “ Facesync: A linear operator for measuring synchronization of video facial images and audio tracks ,” Advances in Neural Information Processing Systems 13, MIT Press, Cambridge Mass., 2000, Fisher et al., “ Learning joint statistical models for audio-visual fusion and segregation ,” Advances in Neural Information Processing Systems 13. MIT Press, Cambridge Mass., 2001.  
         [0004]     It is desired to provide a system and method for detecting events represented by multiple input modes with a single processing method, without a prior understanding of how the events are related.  
       SUMMARY OF THE INVENTION  
       [0005]     The method according to the invention detects objects in a scene over time. Sets of time-aligned features are extracted from multiple signals representing a scene over time. Each signal is acquired using a different modality. Each set of time-aligned features is arranged as a vector in a matrix to which a first transform is applied to produce a compressed matrix. A second transform is applied to the compressed matrix to extract spatio-temporal profiles of objects occurring in the scene. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0006]      FIG. 1  is a flow diagram of a method for detecting temporally related components of multi-modal signals according to the invention;  
         [0007]      FIG. 2  is a block diagram of example audio components according to the invention;  
         [0008]      FIG. 3  is a block diagram of example video components related to the audio components of  FIG. 2 ; and  
         [0009]      FIG. 4  is a block diagram of component weights as a function of time. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0010]      FIG. 1  shows a scene  101 . Multi-modal signals  102 - 104  are acquired  105  from the scene as time series. The signals can be audio, visual, infra-red, radar, magnetic, olfactory, tactile, to name but a few. Digital sampling converts the time series to temporal data sequences  106 .  
         [0011]     Features are extracted  110  from the data sequences using conventional techniques appropriate for the various signaling modalities.  
         [0012]     However, in contrast with the prior art where the features are usually processed separately, the invention stacks all of the extracted features, for each time interval, into a single vector. The order of stacking does not matter, as long as it is consistent for all vectors  111 . The vectors  111  are arranged  120  as temporally ordered columns in a matrix  121 .  
         [0013]     A first transform  131  is applied  130  to the matrix  121  to produce a reduced-dimensionality matrix  132 . A second transform  141  is applied  140  to the reduced-dimensionality matrix  132  to produce temporally aligned component profiles  400 .  
         [0014]     Dimensionality Reduction  
         [0015]     Dimensionality reduction is performed by principal components analysis (PCA). The PCA is a linear transformation that projects the input to make its variates orthonormal. That is x 0 (t)=W 0 ·x(t), where x(t) is the input, x 0 (t) is the output, and W 0  is the linear transformation. The PCA organizes the output in order of variance, so that a first dimension exhibits the greatest variance, and a last dimension the least. In order to reduce dimensionality, only a small number of the higher variance dimensions are retained to produce the matrix  132 .  
         [0016]     Independence Transform  
         [0017]     The second transform  141  uses independent component analysis (ICA). ICA ensures that variates of the input are maximally statistically independent. That is x i (t)=W i ·x r (t), where x r  is the input, x i (t) is the output, and W i  is the linear transformation. A gradient descend method can be used to estimate W i . Upon convergence of the estimated W i , the resulting outputs x i (t) are the component profiles  400  with minimized mutual information. The elements x i (t) are the component weights, and the rows of W are the component bases.  
         [0018]     If the signals from the different modalities are not perfectly time aligned, then an ICA for convolutive mixtures can be used, see U.S. Pat. No. 6,185,309, “Method and apparatus for blind separation of mixed and convolved sources” issued to Attias on Feb. 6, 2001, and Attias et al., “Blind source separation and deconvolution: the dynamic component analysis algorithm,” Neural Computation, 10:1373-1424, 1998. This handles the case where it takes less time for a visual signal to reach a camera co-located with a microphone used to acquire the corresponding audio signal.  
         [0019]     This process decomposes high dimensional input to a smaller set of independent time series. If the input contains a mix of highly correlated and redundant time series, the method removes the correlation and the redundancy to expose the component profiles  400  using a compact description.  
         [0020]     Example Application  
         [0021]     As shown in  FIGS. 2-3  for a real world application, an input video is acquired of a scene where a hand is playing notes on a piano keyboard. The visual signal is sampled at 30 fps, and the soundtrack is sampled at 11025 Hz. Appropriate filtering is applied to extract features.  FIG. 2  shows six audio segments  201 - 206  of a component bases W α , and  FIG. 3  shows the component bases  301 - 306  of the corresponding visual segment W ν .  FIG. 4 , shows the weights x i (t) of the six components 1-6 as a function of time.  
         [0022]     Component 5 has a constant weight value and represents the background term of the scene. The remaining component bases are tuned to the notes of individual keys that have been pressed. The notes corresponding to components 1 and 6 are played once, while the notes corresponding to components 2-4 are played twice.  
         [0023]     This is evident in  FIG. 3  highlighting the keys pressed, and the audio segment in  FIG. 2  roughly tuned to the harmonic series of the notes of each key. The component weights offer a temporal transcription of the melody played, providing the correct timing of the performance.  
         [0024]     Scene Reconstruction  
         [0025]     Using this decomposition is it possible to reconstruct the original scene as is, or in an arbitrary manner. The inverse transform for the above process is A=W + , where the + operator denotes a generalized matrix inverse. The set x i (t) is a set of maximally independent time series that carry enough information to reconstruct the original matrix x(t), by projecting the time series through the transformation A. The quality of the reconstruction depends on how much larger the original dimensionality is from the reduced dimensionality.  
         [0026]     Alternatively, given the highly semantic role of the extracted bases, the component weights can be adjusted to generate a video of a hand playing different melodies on the piano.  
         [0027]     Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.