A Content-Based Color Animal Image Retrieval Method and System

Abstract

The present invention provides a content-based color animal image retrieval method and system. The method includes the following steps: Step 1, inputting a query image as a retrieval target; The color, texture and skeleton feature description of the content, and perform normalization processing, wherein the aforementioned feature template library stores the feature descriptions of different animal colors, textures and skeletons formed based on sample statistics; and step 3, extracting from step 2 The feature description searches based on the R-tree index structure, searches and calculates with the image feature index in the feature index library, and extracts the corresponding picture from the image database according to the matching result. Utilizing the present invention can overcome the influence of illumination changes on image retrieval judgment to a certain extent, and describe the angle and posture semantic information of query animals through skeleton feature analysis during retrieval, and generate retrieval results that are consistent with human visual perception .

Description

technical field [0001] The invention belongs to the technical field of content-based image retrieval, in particular to a content-based color animal image retrieval method and system. Background technique [0002] Content-based image retrieval (CBIR) is an image retrieval technology that uses the visual features of images (color, texture, shape, spatial location, etc.) to search for images. It combines artificial intelligence, object-oriented technology, cognitive psychology, database and other multidisciplinary knowledge, and automatically extracts the underlying visual features and high-level semantic features of images, thus avoiding a series of problems generated by image retrieval systems based on manual annotation. The problem makes the application of large-scale image database system more realistic. Therefore, with the rapid growth of various image resources in the Internet age, CBIR has become the mainstream technology in the field of image retrieval research in the ...

AI Technical Summary

Problems solved by technology

[0009] 1. Most of the existing CBIR technologies based on the underlying features of images such as color, texture, and inflection point features are extremely sensitive to image scale and brightness changes

On the other hand, the animal images in the natural background can easily change the above-mentioned underlying characteristics of the animal body surface due to reasons such as illumination and occlusion.

Therefore, the CBIR technology that uses the underlying features of images such as color, texture, and inflection point features will cause a high probability of misjudgment when retrieving color animal images;

[0010] 2. Most of the existing CBIR technologies based on visual features containing certain semantic information such as object shape and spatial position are mainly aimed at grayscale images, and there is relatively little work on segmenting color images.

On the other hand, this type of retrieval technology mainly relies on the quality of image segmentation, and the existing image segmentation methods are prone to over-segmentation problems for animal image segmentation under changing illumination conditions, which greatly reduces the precision rate;

[0011] 3. CBIR based on the high-level semantics of images is still in the research stage. Although the academic community is working hard to study the high-level semantic expression of images, there is still no general recognition of what the high-level semantics of images are and how to express these semantics. Mathematical or perceptual models, there is still a certain distance from the application;

[0012] 4. When most of the existing CBIR technologies are applied to the retrieval of color animal images, they lack the characteristic description of animal posture, and the retrieval results cannot well meet the retrieval requirements.

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