Image recognition method
1. Automatically find graphic text: You can detect whether there is an image file that can be displayed through two HTML tags, namely IMG SRC and HREF. IMG SRC means "display the image file below", and HREF means "there is a link below". These two tags usually point to an image file. Search engines can judge whether the linked file is an image file by checking the file extension. If the file extension is. GIF or. JPG, this is a displayable image.
2. Manual intervention to find out the images and classify them: that is, manual selection of online images and websites. This method can produce an accurate query system, but the labor intensity is too great, which limits the number of processed images. Because images are different from texts, people need to interpret their meanings according to their own understanding, so image retrieval is much more difficult than text query and matching. At present, most image search engines support keyword retrieval and classified browsing, and some can also provide visual attribute retrieval, but they also have limitations. Their main retrieval methods are as follows:
A. Image-based external information: that is, according to the external information such as the file name or directory name, path name, link, ALT tag and the text information around the image, this is the most commonly used method in image search engines at present. After finding an image file, the image search engine determines the content of the file by looking at the file name or path name, but it depends on the description degree of the file name or path name.
B. Feature description based on image content: This is a semantic matching. It is necessary to describe and classify the contents of images (such as objects, backgrounds, composition, color features, etc.). ) Manually and give descriptive text. When searching, you will mainly search for your search term in these descriptors. This query method is relatively accurate, generally speaking, it can obtain better accuracy. However, it requires manual participation, which is labor-intensive, thus limiting the number of images that can be processed, and it requires certain norms and standards, and the effect depends on the accuracy of manual description.
C. Feature extraction based on image morphology: The color, shape, texture and other features of images are automatically extracted by image analysis software, and a feature index library is established. Users can find images with similar features only by describing the general features of the images to be searched. This is a kind of mechanical matching based on image feature hierarchy, which is especially suitable for query requirements with clear retrieval objectives (such as trademark retrieval). The result is also closest to the user's requirements. But at present, this mature retrieval technology is mainly used in image database retrieval, and there are still some difficulties in applying this retrieval technology in online image search engine.