Guiding Eye Movements For Feature Based Shape Matching

We introduce a novel method for shape-based image database search that uses saccadic targeting for local feature choice. A simulated multiresolution sensor is directed toward salient regions of an image in a series of saccadic movements. At each fixation point 1 a region of the retinal image is stored for later matching by correlation. The utility of this approach is demonstrated on an 86 image database

A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise

Gabor Barcodes for Medical Image Retrieval

In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle to achieve these goals. One of the recently introduced binary descriptors for tagging of medical images are Radon barcodes (RBCs) that are driven from Radon transform via local thresholding. Gabor transform is also a powerful transform to extract texture-based information. Gabor features have exhibited robustness against rotation, scale, and also photometric disturbances, such as illumination changes and image noise in many applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework for the image annotation. To find the most discriminative GBC for a given query image, the effects of employing Gabor filters with different parameters, i.e., different sets of scales and orientations, are investigated, resulting in different barcode lengths and retrieval performances. The proposed method has been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray images for indexing, and 1,733 x-rays images for testing. A total error score as low as $351$ ($\approx 80\%$ accuracy for the first hit) was achieved.Comment: To appear in proceedings of The 2016 IEEE International Conference on Image Processing (ICIP 2016), Sep 25-28, 2016, Phoenix, Arizona, US

IMAGE SEARCH ENGINE BASED ON COMBINED FEATURES OF IMAGE SUB-BLOCKS

In this paper we propose a new and efficient technique to retrieve images based on sum of the values of Local Histogram and GLCM (Gray Level Co-occurrence Matrix) texture of image sub-blocks to enhance the retrieval performance. The image is divided into sub blocks of equal size. Then the color and texture features of each sub-block are computed. Most of the image retrieval techniques used Histograms for indexing. Histograms describe global intensity distribution. They are very easy to compute and are insensitive to small changes in object translations and rotations. Our main focus is on separation of the image bins (histogram value divisions by frequency) followed by calculating the sum of values, and using them as image local features. At first, the histogram is calculated for an image sub-block. After that, it is subdivided into 16 equal bins and the sum of local values is calculated and stored. Similarly the texture features are extracted based on GLCM. The four statistic features of GLCM i.e. entropy, energy, inverse difference and contrast are used as texture features. These four features are computed in four directions (00, 450, 900, and 1350). A total of 16 texture values are computed per an image sub-block. An integrated matching scheme based on Most Similar Highest Priority (MSHP) principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. Sum of the differences between each bin of the query and target image histogram is used as a distance measure for Local Histogram and Euclidean distance is adopted for texture features. Weighted combined distance is used in retrieving the images. The experimental results show that the proposed method has achieved highest retrieval performance

Color Image Clustering using Block Truncation Algorithm

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters