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

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

Object Detection using Deep Learning with Hierarchical Multi Swarm Optimization

Till now there is a huge research had in the field of visual information retrieval, but with the growth of data and with less processing speed we are not meeting the needs of current problem. The main focus of this paper is to identify the objects with salient features and object highlighting. Till now object identification is done with the pixel based or with the region based. Different methodologies are compared in this work and they will be processed with the learning work. Multi scale contrast is one of the pixel based technology where object borders are identified but not the object. This can be done with the histogram contrast. Still it is not covering all the features of the object and it is not clear in identifying the objects at high contrast regions. To solve this issue region based contrasting method is used which is the better solution for all this object identification. After extracting the features and identifying the object, now auto classification or identification of the object should be done. The other part of the work mainly concentrates on the learning system which uses most popular neural network algorithms. Identifying the drawbacks of neural network algorithms and proposing the new methodology identify the objects is done in this paper

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

Robust localization and identification of African clawed frogs in digital images

We study the automatic localization and identification of African clawed frogs (Xenopus laevis sp.) in digital images taken in a laboratory environment. We propose a novel and stable frog body localization and skin pattern window extraction algorithm. We show that it compensates scale and rotation changes very well. Moreover, it is able to localize and extract highly overlapping regions (pattern windows) even in the cases of intense affine transformations, blurring, Gaussian noise, and intensity transformations. The frog skin pattern (i.e. texture) provides a unique feature for the identification of individual frogs. We investigate the suitability of five different feature descriptors (Gabor filters, area granulometry, HoG,1 dense SIFT,2 and raw pixel values) to represent frog skin patterns. We compare the robustness of the features based on their identification performance using a nearest neighbor classifier. Our experiments show that among five features that we tested, the best performing feature against rotation, scale, and blurring modifications was the raw pixel feature, whereas the SIFT feature was the best performing one against affine and intensity modifications

A fast image retrieval method designed for network big data

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results

Image Retrieval Based on Edge Histogram Descriptor of MPEG-7

A major research area in computer vision is content-based image retrieval. MPEG-7 sets up a list of descriptions of the structured image content. We examine the weakness and lack of retrieval approaches based on global characteristics in this study by incorporating the commonly used feature descriptors of MPEG-7. In the meantime, to satisfy user requirements for assessing spatial information similarities, an image retrieval approach based on texture region features for MPEG-7 is recommended. Retrieval tests show the validity and efficiency of our approach. This paper also defines our approach to color quantization, extraction, and matching processes of features and so on in depth

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The result

Um mecanismo para automatizar a criaçao dos metadados das imagens de bibliotecas digitais e prover buscas por conteúdo

Orientador: Marcos S. SunyeDissertaçao (mestrado) - Universidade Federal do Paraná, Setor de Ciencias Exatas, Programa de Pós-Graduaçao em Informática. Defesa: Curitiba, 2005Inclui bibliografia e anex