Searching of images stored in a database using content and pixel based methods

In the paper we consider combined pixel and content based searching of images. We propose an application of Stone’s method of progressive wavelet correlation using Fourier methods for pixel-based searching of images stored in a database. The proposed interface between the Matlab work-space and the database is described. The Oracle Database and the IBM QBIC are used for investigation purposes

Application of the Progressive Wavelet Correlation in Image Retrieving

In the paper we apply progressive wavelet correlation along with Fourier methods for searching and retrieving images stored in a database. The searching consists of three incremental steps, each of which quadruples the number of correlation points. The process can be halted at any stage if the intermediate results indicate that the correlation will not result in a match. We perform a series of image search experiments that cover the following scenarios: (A) the given image is present in the database; (B) copies of the given image are present but with different names; (C) similar (but not identical) images are present; and (D) the given image is not present. Experiments are performed with data bases up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases

Performance of the Progressive Wavelet Correlation for Image Retrieval

In the paper we present our experience with using progressive wavelet correlation for image retrieval. We perform a series of image search experiments that cover the following scenarios: (A) the given image is present in the database; (B) copies of the given image are present but with different names; (C) similar (but not identical) images are present; and (D) the given image is not present. Experiments are performed with data bases up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases

Applying of the Combination of Content-Based Search and Progressive Wavelet Correlation in Image Retrieving

An algorithm for search and retrieval of images from massive image collections is developed. The algorithm consists of two phases. The first phase uses well-known methods of image searching by descriptors based on the content of the searched image. In the second phase the progressive wavelet correlation method is applied on the small number of image candidates selected in previous search phase. The final search result is the wanted image, if it is in the data base. Experiments are performed with data bases of 1000 and 10 000 images

Retrieving Images Using Content-Based Search and Progressive Wavelet Correlation

The following study looks at our experience in retrieving images from a database via a combination of content based search and progressive wavelet correlation. We distinguish two typical databases. One of them is comprised of images that are visually similar to the input; the other has dissimilar images. We examine ways of choosing the threshold value in either case. In our study we use Oracle database and IBM QBIC

Our experience with image retrieval using progressive wavelet correlation

We present our experience with using progressive wavelet correlation for image retrieval from image databases. We perform a series of image search experiments that cover the following scenarios: (A) the given image is present in the database; (B) copies of the given image are present but with different names; (C) similar (but not identical) images are present; and (D) the given image is not present. Experiments are performed with data bases up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases

Applying of the Pseudoinverse Matrix in the Removal of Blur in Images

A method for digital image restoration, based on the pseudoinverse matrix, has many practical applications. We apply the method to remove blur in an image caused by uniform linear motion. This method assumes that linear motion corresponds to an integral number of pixels. Compared to other classical methods, this method attains higher values of the Improvement in Signal to Noise Ration (ISNR) parameter and of the Peak Signal-to-Noise Ratio (PSNR). We give an implementation in the MATLAB programming package

Performance of the Hybrid Method of Image Retrieval

We study the performance of the hybrid method of image retrieval (content-based followed by pixel-based retrieval) from image databases. We carry out a series of image search experiments that cover the following scenarios: a) the given image is present in the database; b) copies of the given image are present but with different names; c) similar (but not identical) images are present; and d) the given image is not present. Experiments are performed with databases of up to 1000 images, using the Oracle database and the Matlab component Database Toolbox for operations with databases

Application of Non-Iterative Method in Digital Image Restoration

A method for digital image restoration, based on the algorithm of Lagrange multipliers, has many practical applications. We apply the method to remove blur in an image caused by uniform linear motion. This method assumes that linear motion corresponds to an integral number of pixels. Compared to other classical methods, this method attains a higher value of the Improvement in Signal to Noise Ration (ISNR) parameter and a lower value of the Mean Square Error (MSE) parameter. Moreover, this method is more time-efficient. We give an implementation in the MATLAB programming package

Retrieving Images Using Content-Based Followed by Pixel-Based Search

In this paper we present our experience in retrieving images from a database by applying the combination of content and pixel-based methods. We consider two typical databases: One of them contains images visually similar to the input and the other one doesn’t. We investigate how to choose the value of the threshold in both cases. The Oracle Database and the IBM QBIC are used for investigation purposes