OCRM: Optimal Cost Region Matching Similarity Measure for Region Based Image Retrieval

Rao, N. Gnaneswara; Sravani, T.; Kumar, V. Vijaya

doi:10.14257/ijmue.2014.9.4.34

Cited by 12 publications

(2 citation statements)

References 23 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kennedy et al [10] have discussed the importance of Map-Reduce approach by labeling millions of images and applying the Nearest Neighbour algorithm to it. Shi et al, [11] also explained the same usage of Map-Reduce for CBIR [27,28] for applying nearly 400,000 images almost. Mohammed [12] explained the concept of high-performance computing and also analyzing the performance of remote sensing data using Hadoop.…”

Section: Related Workmentioning

confidence: 97%

Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

View full text Add to dashboard Cite

Now-a-days, sensing of remote satellite data processing is a very challenging task. The current development of satellite technology has led to explosive growth in quantity as well as the quality of the High-Resolution Remote Sensing (HRRS) images. These images can sometimes be in Gigabytes and Terabytes, which is heavy to load into the memory and also takes more time for processing. To address the challenges of processing HRRS images, a distributed map Reduce framework is proposed in this paper. This paper reflects Map-reduce as a distributed model using the Hadoop framework for processing large amounts of images. To process large amounts of images, block-based and size-based methods are introduced for effective processing. From the experiments, the proposed framework has proven to be effective in performance and speed.

show abstract

Section: Related Workmentioning

confidence: 97%

Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…The watermarking has been developed to defense digital image again against illegal copyright violation [16][17][18][19][20][21][22][23][24]. The best method is to guarantee to provide images with maximal fidelity and robust against different attacks [25][26][27][28]. In this paper we study watermarking with bit plane slicing.…”

Section: Introductionmentioning

confidence: 99%

Watermarking Application Using Bit Plane Allocation

Jeon¹

2014

IJSIA

View full text Add to dashboard Cite

This paper studies about bit plane watermarking. The watermarking has been studied to protect digital image against illegal copyright action. Generally speaking, adding watermarking symbol in least significant bit (LSB) is desirable. However, sometimes watermarking information in LSB can be lost during the compression and transmission. Therefore, well designed watermarking technique is necessary. In this paper, we show how to design watermarking application, and compare watermarking performance on different bit plane. Two datasets were used; they are Kodak and McM datasets.

show abstract