Commodity recommendation method and system based on video content
A technology of product recommendation and video content, applied in the field of product recommendation based on video content, can solve the problem of low accuracy of recommendation results, and achieve the effect of reducing the amount of calculation, speeding up the processing speed, and improving the accuracy
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Embodiment 1
[0030] refer to figure 1 , which shows a flow chart of a video content-based product recommendation method according to an embodiment of the present invention. This embodiment may specifically include the following steps:
[0031] Step 101, extract key frames from the target video.
[0032] In this embodiment, the key frame extraction of the target video can be realized by using a frame difference method based on color (or histogram), a method based on motion analysis, or a method based on video frame clustering. Among them, the key frames of the video are extracted through the video frame clustering method, that is, the frames of the video shots are divided into several categories through cluster analysis, and the points closest to the cluster center are selected to represent the cluster points, and finally the key frame set of the video sequence is formed. . It should be noted that this embodiment does not limit the specific method used for extracting the key frames of the...
Embodiment 2
[0047] On the basis of the above embodiments, this embodiment continues to discuss the video content-based commodity recommendation method.
[0048] refer to figure 2 , which shows a flow chart of a video content-based product recommendation method according to an embodiment of the present invention. This embodiment may specifically include the following steps:
[0049] Step 201, extract key frames from the target video.
[0050] In this embodiment, extracting key frames from the target video may specifically include the following sub-steps:
[0051] Sub-step 1: Shot detection is performed on the target video, and several shots are extracted. First, extract the color histogram features of each frame of the target video; secondly, calculate the similarity of the color histograms of adjacent two frames according to the color histogram features of each frame; again, when the adjacent two frames When the similarity of the color histogram is smaller than the similarity threshol...
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