A Comparative Analysis of Content based Image Retrieval

Abstract

With the development of multimedia technology, the rapid increasing usage of large image database becomes possible. To carry out its management and retrieval, Content-Based Image Retrieval (CBIR) is an effective method. It will be very difficult to manage this database of images stored at the remote servers. The right tool will be required which can process these images for different operations. These operations include searching etc. It will be difficult to classify the images into groups and then search each class for providing the image as the information against the user request query. The content based image retrieval is the most suitable way to identify the image from the large repository. It will search the image from the large set of images based on contents rather than the image name. It will be having less time to search the image from the large repository when the image is retrieved using content based. In the current research the hybrid approach for content based image retrieval is performed. This proposed procedure will be in the first step perform the classification of the image into multiple classes. The classes are prepared based on the attributes values.

A Speed-up Channel Attention Technique for Accelerating the Learning Curve of a Binarized Squeeze-and-Excitation (SE) Based ResNet Model

“…Deep learning has revolutionized various applications within the realm of computer vision, with Convolutional Neural Networks (CNNs) emerging as a predominant architecture. Modern high-performance CNNs often consist of recurring blocks with identical structures [1][2][3][4][5][6][7], leveraging principles from residual learning [8][9][10], and utilizing depthwise separable convolutions [11]. While these networks have demonstrated an impressive performance with 32-bit representation for the weight and the activation, it poses significant challenges to deploy them in real-world scenarios, especially on stringent power and memory-footprint constrained devices.…”