Abstract: Content-based image retrieval (CBIR) is image retrieval approach which allows the user to extract an image from a large database depending upon a user specific query. An efficient and effective image retrieval performance is achieved by choosing the best transform and classification techniques. Currently available transform techniques such as Fourier Transform, Cosine Transform, and Wavelet Transform suffer from discontinuities such as edges in images. To overcome this problem, a technique called Ripplet Transform (RT) has been implemented along with the neural network based classifier called Multilayered perceptron (MLP) for finding an effective retrieval of image. Classification using multilayered perceptron (MLP) with the Manhattan Distance measure showed varying experimental results for dimensions of Images. The performance of various Transform is compared to find the of particular wavelet function for image retrieval.
Keywords: Content-based image retrieval (CBIR), Ripplet transforms (RT), Multilayered Perceptron (MLP), Edge Histogram Descriptor, Feature Vector, Similarity Check.