Local Descriptor Extraction Method, Image Retrieval Method and Image Matching Method

Abstract

The invention provides a local descriptor extracting method, an image searching method and an image matching method. The local descriptor extracting method comprises the following steps of obtaining a core point of interest of an image, wherein the core point of interest is a pixel point which is used for representing the key characteristic of the image; determining the subzone radius of the core point of interest and the main direction corresponding to the core point of interest; and according to the subzone radius and the main direction, obtaining the local descriptor of the image. The local descriptor extracted by the method is used for more accurately reflecting the construction image of the image shape, so the accuracy of a searching result using the local descriptor for the post searching is higher.

Description

technical field [0001] The invention relates to image retrieval technology, in particular to a method for extracting local descriptors, an image retrieval method and an image matching method. Background technique [0002] Currently, visual retrieval methods mainly use the extraction of scale-invariant descriptors (ScaleInvariantFeatureTransform, referred to as: SIFT) or fast and robust scale-invariant features (SpeededUpRobustFeatures, referred to as: SURF) in images for image retrieval. [0003] However, for images / pictures with sparse texture features, or line maps used to map human thinking features, these images themselves lack texture characteristics, and SIFT and SURF cannot obtain enough points of interest. Therefore, if SIFT and SURF are used for image The retrieval seriously affects the performance and efficiency of the retrieval system. [0004] People in the industry have proposed a shape context descriptor (ShapeContextDescriptor, referred to as: SCD) for hand-p...

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Problems solved by technology

[0003] However, for images / pictures with sparse texture features, or line maps used to map human thinking features, these images themselves lack texture characteristics, and SIFT and SURF cannot obtain enough points of interest. Therefore, if SIFT and SURF are used for image The retrieval seriously affects the performance and efficiency of the retrieval system

[0004] People in the industry have proposed a shape context descriptor (ShapeContextDescriptor, referred to as: SCD) for hand-painted pictures / hand-painted images that lack texture information but have clear outline information. Compared with SIFT and SURF, this SCD pays attention to shape features, but due to its own construction method In order to densely sample the contour of the image and count the distance and angle relationship between the sampling points, this construction method makes the expressiveness of the descriptor focus on the edge contour. Therefore, the above-mentioned SCD contains a large number of lines and information inside the shape. The image cannot reflect the real shape information of the image when searching, resulting in the failure of matching retrieval

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