Well, in the field of CBIR, there are several popular algorithms that have proven to be effective. One of the most widely used ones is the Color Histogram technique, which represents an image using the distribution of colors in the RGB color space. Another commonly used method is the Scale-Invariant Feature Transform (SIFT), which detects and describes distinctive features in an image. Additionally, techniques based on Convolutional Neural Networks (CNNs) have gained a lot of attention recently due to their ability to learn rich image representations.

When it comes to CBIR, there isn't necessarily a one-size-fits-all algorithm that is universally effective. The choice of technique depends on various factors such as the nature of the images, the available computational resources, and the specific requirements of the application. That being said, some of the popular algorithms include the Bag of Visual Words (BoVW) approach, which models images as histograms of visual words, and the Deep Convolutional Features (DCF) method, which leverages pre-trained CNNs to extract image embeddings. It's important to experiment and evaluate different techniques to find the most suitable one for your particular use case.