A Novel Image Retrieval Based on Multi Resolution Color and Texture Features of Image Sub-blocks

Kavitha, C.; Rao, M. Babu; Rao, B. Prabhakara; Govardhan, A.

doi:10.1007/978-3-642-25734-6_52

Cited by 6 publications

(12 citation statements)

References 3 publications

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“…In disaster situations, victims are grouped into four categories, coded red, yellow, green or no color. Then, the image of each victim is partitioned into sub-blocks (Kavitha et al 2011) by dividing up the whole image into pieces (or chunks). For efficient delivery of both high-and lowpriority pieces, we make each piece have a different data size and number of pixels depending on the color code assigned to the piece.…”

Section: Assigning Priorities To Imagesmentioning

confidence: 99%

Priority medical image delivery using DTN for healthcare workers in volcanic emergency

Ashar

Suwa

Arakawa

et al. 2016

Sci Phone Appl Mob Devices

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In this paper, targeting eye injuries caused by volcano disaster, we propose a medical image delivery service that streamlines the delivery of victim image data from a disaster area to specialist doctors in city hospitals using the Delay Tolerant Network (DTN). The service is used for an emergency response to provide quick feedback to healthcare workers after images are received by a hospital. With the received images, specialist doctors diagnose the type and seriousness of the eye injury in those images and provide appropriate medical instructions to healthcare workers. To reduce image delivery delay, it is desirable to send medical images to doctors based on image priority. For this purpose, we propose an image prioritization method in which an image is divided into pieces, and each piece is assigned a priority based on its content (for example, the severity of the injury), aiming to deliver high-priority pieces faster. Based on the priorities assigned to the pieces, we propose a message priority forwarding scheme for pieces in a DTN environment, where higher priority pieces are assigned more bandwidth and transmitted with higher resolution. Also, taking into account actual practice in a disaster area, we design and implement an application for Android devices. Through computer simulations supposing a volcano disaster scenario involving Mount Merapi in Indonesia, we confirmed that the proposed delivery service significantly shortens the image delivery time.

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Section: Assigning Priorities To Imagesmentioning

confidence: 99%

Priority medical image delivery using DTN for healthcare workers in volcanic emergency

Ashar

Suwa

Arakawa

et al. 2016

Sci Phone Appl Mob Devices

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“…GLCM is one of the popular statistical methods for texture descriptor which examines the distribution of the gray intensities in different displacement and orientation (Kavitha et al, 2011), (Wang et al, 2012). The color image is converted into gray scale image and it is resized to 256 x 256.Quantization takes place to normalize the values into a common range [1-N].This process is performed using the following formula…”

Section: A Co-occurrence Matrixmentioning

confidence: 99%

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

2016

Rev. Téc. Ing. Univ. Zulia

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Content Based Image Retrieval is an active way of searching, browsing, or retrieving images from a large image repository. Researchers are intensely competing for developing efficient and precise image retrieval tool which can be applied to various image searching devices that run on different platforms. By applying static clustering, similarity between query image and each cluster is determined to find the closest cluster which has greater likelihood. To obtain more relevant images, it searches not only a single cluster but also the nearby clusters and when the database size increases, all the clusters get updated to reflect the changes in the database. To overcome this problem the proposed method develops an efficient CBIR system that uses rule based Boolean query. It does not generate clusters, instead a dynamic rule has been derived using query image features to retrieve the primary collection of relevant images which has higher semantics. Instead of using dissimilarity distances directly this system determines the subset of images and then it is refined to yield the topmost closest images by finding the shortest distance using Euclidean distance. Gray-level co-occurrence matrix is used to determine texture features such as energy, entropy, contrast and homogeneity. Region based method is used for shape features such as area, perimeter, solidity and circularity of a largest connected component. Dominant color descriptors in RGB space are used for extracting the color feature. Performance can be compared to an existing system by evaluating the time, retrieval precision and recall. The experiment is performed on Corel dataset and it shows superior performance than the previous system.

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“…To extract the texture vector, all images were converted to grey scale images as [24] defined textures as, "Texture is an attribute representing the spatial arrangement of the grey levels of the pixels in a region or image", and many researchers extracted texture features from grey scale images as well [25] [26]. Images then were decomposed using the DWT method for two levels and the resulted (4) sub bands (LL2, LH2, HL2, HH2) were used to extract the texture vectors by calculating the mean value for each (8 × 8) block of pixels, the block size was experimentally decided as the size of each of the resulted sub bands was (64 × 64).…”

Section: Extracting Texture Vectorsmentioning

confidence: 99%

Content-Based Image Retrieval Using SOM and DWT

Huneiti¹,

Daoud²

2015

JSEA

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Content-Based Image Retrieval (CBIR) from a large database is becoming a necessity for many applications such as medical imaging, Geographic Information Systems (GIS), space search and many others. However, the process of retrieving relevant images is usually preceded by extracting some discriminating features that can best describe the database images. Therefore, the retrieval process is mainly dependent on comparing the captured features which depict the most important characteristics of images instead of comparing the whole images. In this paper, we propose a CBIR method by extracting both color and texture feature vectors using the Discrete Wavelet Transform (DWT) and the Self Organizing Map (SOM) artificial neural networks. At query time texture vectors are compared using a similarity measure which is the Euclidean distance and the most similar image is retrieved. In addition, other relevant images are also retrieved using the neighborhood of the most similar image from the clustered data set via SOM. The proposed method demonstrated promising retrieval results on the Wang Database compared to the existing methods in literature.

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A Novel Image Retrieval Based on Multi Resolution Color and Texture Features of Image Sub-blocks

Cited by 6 publications

References 3 publications

Priority medical image delivery using DTN for healthcare workers in volcanic emergency

Priority medical image delivery using DTN for healthcare workers in volcanic emergency

CBIR: Primary Collection of Relevant Image Selection using Rule Based Boolean Query

Content-Based Image Retrieval Using SOM and DWT

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