Image feature extraction method and device and electronic equipment
An image feature and extraction method technology, applied in the field of image processing, can solve problems such as slow speed and loss of accuracy, and achieve the effects of avoiding loss of accuracy, improving accuracy and improving computing performance
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Embodiment 1
[0027] First, refer to figure 1 An example electronic device 100 for implementing a method and apparatus for extracting image features according to an embodiment of the present invention will be described.
[0028] Such as figure 1 Shown is a schematic structural diagram of an electronic device. The electronic device 100 includes one or more processors 102, one or more storage devices 104, an input device 106, an output device 108, and an image acquisition device 110. These components pass through a bus system 112 and / or other forms of connection mechanisms (not shown). It should be noted that figure 1 The components and structures of the electronic device 100 shown are only exemplary, not limiting, and the electronic device may have figure 1 Some components shown may also havefigure 1 Other components and structures are not shown.
[0029] The processor 102 can be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate arra...
Embodiment 2
[0036] see figure 2 A schematic flow chart of a method for extracting image features is shown, the method is applied to the above-mentioned electronic device, specifically, it can be executed by a processor in the electronic device, and the method mainly includes the following steps S202 to S204:
[0037] Step S202, acquiring an image to be processed and a preset convolution kernel.
[0038] Wherein, the image to be processed includes an original image or a feature map, and the data types of the image to be processed and the convolution kernel are fixed-point types. The original image can include the initial image captured by the image acquisition device, downloaded from the network, locally stored or manually uploaded, such as an RGB image, and the feature map can include the next layer obtained after convolution operation on the initial image or the intermediate feature map feature map. Wherein, the fixed-point type may include int8 type, int16 type, or int32 type, etc., ...
Embodiment 3
[0065] On the basis of the foregoing embodiments, this embodiment provides a specific example of applying the foregoing image feature extraction method, see Figure 4 A specific schematic diagram of convolution processing shown in the embodiment of the present invention uses floating-point calculations for input conversion, weight conversion, batch matrix multiplication and output conversion, and the data types of the input conversion matrix, weight conversion matrix and output conversion matrix are all Take the floating-point type as an example to illustrate.
[0066]In a specific implementation, the data types of the image to be processed and the convolution kernel are both fixed-point and int8 types, and the input conversion matrix of the floating-point type is used to perform input conversion on the image to be processed (the calculation method adopted is floating point calculation) to obtain the input conversion result of the floating point type, and use the weight conver...
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