Memory-guide simulated pattern recognition method
A memory-guide simulated pattern recognition method, wherein time sequence information in a memory prior to the current moment is introduced to detect motion information by means of pattern recognition using samples in the sequence as a reference is described. A memory invocation mechanism in a human brain is simulated, and different memory segments are used as elements to detect motion changes in multiple memory segments and the corresponding motion states; a memory attenuation mechanism is simulated, and motion detection results in multiple segments are combined to enhance motion change information in the current moment and form a motion state in a continuous time sequence, so as to form a motion detection result of the current moment as a pattern recognition result.
1. A computer implemented memory-guide simulated pattern recognition method, implemented via a processor, comprising the following steps:
S1: simulating, via the processor, a memory invocation mechanism and a process thereof to segment a historical time sequence and then combine the historical time segment x n i with a frame at the current moment x n to form a sequence segment as an element for pattern recognition, wherein i is a memory storage amount and n is a time step associated with the current moment;
S2: simulating, via the processor, a visual motion saliency detection mechanism and a process thereof to extract motion saliency in each sequence segment and obtain motion information in the short-term sequence by detection based on a visual motion significance model determined based on a time Fourier transformation, wherein fluctuation of a phase spectrum in a time sequence frequency spectrum corresponds to the change of the sequence information in a time domain; and
S3: simulating, via the processor, a memory decline mechanism and a process thereof to weigh the motion information in different segments, fuse the motion information among all the sequence segments, and output the motion information at the current moment and a motion trajectory in the entire time sequence as a pattern recognition result comprehensively.
2. The memory-guide simulated pattern recognition method according to claim 1 , wherein a sequence segment based motion detection strategy is proposed by simulating the memory invocation mechanism, which segments a historical sequence and then uses a combination of a sequence segment and the frame at the current moment as an element for motion detection;
specifically, for an image frame at the current moment t in a video sequence, successive frames among 1, 2, . . . , t−1 are segmented according to a length k, and the motion changes in each sequence segment are detected by simulating the visual motion saliency mechanism and a detection result is a pattern recognition result for each sequence segment.
3. The memory-guide simulated pattern recognition method according to claim 1 , wherein in the step of simulating the memory decline mechanism, for the motion detection result in each segment and in view of a time delay between a moment of the sequence segment and the current moment, the motion detection result in the sequence segment with a larger time delay is deemed to have a smaller temporal correlation with an event of the current moment, and a corresponding weight value assigned is smaller;
on the contrary, the motion detection result in the sequence segment with a smaller time delay is deemed to have a stronger temporal correlation with the event of the current moment, and a corresponding weight value assigned is larger; and
a motion detection accuracy at the current moment can be improved by cumulatively fusing the weighted motion detection results, and the motion trajectory over a whole time interval in the memory can be obtained, so as to comprehensively obtain an overall pattern recognition result.