In recent years, some scholars have proposed local features for image similarity detection. Usually, similar images are created by editing transformation similarity detection accuracy is generally relatively low based on the global features of the image content. For example, image similarity detection with color histogram as global feature is very sensitive to the illumination of the image. However, due to the singularity of its feature selection and the roughness of the description image, the global feature is very susceptible to edits and local transformations. Because the number of feature points is small, the calculation speed of image content similarity detection based on global feature is usually very fast. Common global features include color histograms, texture features, and block features. The global feature of the image refers to the use of one or a few feature vectors to represent whole image content. According to the adopted feature, image similarity detection methods can be divided into two categories, namely, global-feature-based detection methods and local-feature-based detection methods. Image similarity detection is to judge the similarity of visual content by matching the image. Examples of some similar images are shown in Figure 1. Similar image is a set of images obtained from an image of the same scene or the same object taken from different environmental conditions such as different angles or different lighting conditions and edited transformations of the same original image through different ways. Image similarity detection is a hot issue in the field of multimedia information processing. The experimental results show that the proposed algorithm can significantly improve the detection speed compared with the traditional algorithm based on local feature detection under the premise of guaranteeing the accuracy of algorithm detection. The image similarity detection result is obtained by comparing the sparse coefficients. The SIFT feature vector of the image is sparse-coded with the overcomplete dictionary, and the sparse feature vector is used to build an index. Firstly, the SIFT feature of the image is extracted as a training sample to complete the overcomplete dictionary, and a set of overcomplete bases is obtained. The algorithm improves the image similarity matching speed by sparse coding and indexing the extracted local features. I hope you have learned something out of this.Aiming at the problem that the image similarity detection efficiency is low based on local feature, an algorithm called ScSIFT for image similarity acceleration detection based on sparse coding is proposed. įollow me for more such articles and implementations on different real-world case studies in data science! You can also connect with me through LinkedIn and Github ipynb for the full code snippet of this case study in my Github repository. Step-4: Using KNN model finding N similar images using predict images and finally plotting the result Step-3: From the extracted features finding the label to which that image belongs using K-Means clustering. Step-2: Using that array finding the feature from the intermediate layers of the trained AutoEncoder model. Step-1: Taking either filename or URL and converting that image into an image array.
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