Image searching processing has numerous applications. An Image searching processing algorithm which finds the corresponding images with the help of SIFT descriptors and Hessian affine detector is proposed. The query image is taken. The query image features were calculated with the help of SIFT and Hessian affine detector. The SIFT descriptor extracts the edge based informations from the image and the Hessian affine descriptor gives the spatial context informations. In SIFT descriptors the feature points are detected, an orientation histogram is formed from the gradient orientations of sample points within a normalized region. The highest peak in the histogram is detected and regarded as the dominant orientation based on which the SIFT descriptor is computed. Usually any other local peak within eighty percent of the highest peak is also used to create a descriptor. Hence, there may be multiple SIFT descriptors computed based on different dominant orientations for a single local feature region. In Hessian descriptor the position of the pixels and their dominant orientations were calculated which is invariant to the rotational transformations of the images. The test image feature vector is matched with the train image feature vector with the help of calculating the distance between the feature vectors. The id of the trained feature vectors that are having minimum distance were identified and the images were retrieved from the database.