Comparison of Different Distance Metrics to Find Similarity between Images In CBIR System

Content based image retrieval use low level feature (color, shape, texture) of image for retrieving similar image from image database. This paper presents a novel system for texture feature extraction from grayscale images using gray level co-occurrence matrix (GLCM). It works on statistical texture feature of image. Texture feature of image is referred to as repeated homogenous pattern in an image. This texture feature is classified into three categories Statistical, structural and spectral. Among these we extract second order statistical texture feature from image using GLCM. These features are Energy, correlation, contrast, homogeneity, entropy. Different distance metrics are used to find the similarity between images. The experiment is conducted on own texture database. Accuracy of result and time complexity of design algorithm for CBIR system is calculated. DOI: 10.17762/ijritcc2321-8169.16043

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding followed by morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture feature vectors is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.Comment: 7 page

Survey of Object Detection Methods in Camouflaged Image

Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this research paper authors presented survey of camouflage detection methods for different applications and areas

A Hybrid Deep Learning Approach for Texture Analysis

Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of experts provides stability in classification rates among different datasets