Interactive Semantic Image Retrieval

Patil, Pushpa B.; Kokare, Manesh

doi:10.3745/jips.2013.9.3.349

Cited by 13 publications

(4 citation statements)

References 23 publications

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“…Wavelet analysis is one of the most important methods for extracting a signal from any data [17][18][19][20]. The Wavelets de-noising application has been widely used.…”

Section: Noise Reduction By Wavelet Threshold Functionmentioning

confidence: 99%

Design and implementation of an automated monitoring system

2016

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One of the main limitations of existing monitoring systems is their dependency on the continuous recording of video data. As a result, the system consumes huge power and needs large memory to store this data. The aim of this paper is to present an automated monitoring system that can save memory, as well as measure the target distance precisely based on the motion sensors and ultrasonic sensor. The target is detected by motion sensors and then an ultrasonic sensor is used to measure the target distance. The cross-correlation technique is used to calculate the distance of targets using ultrasonic signal. To obtain high measuring accuracy, noise reduction technique and temperature effect on velocity have been adopted. A series of real-world experiments are conducted with different targets and target positions. The experimental results show that using a noise reduction technique and temperature compensation improve the accuracy of the designed system.

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“…Wavelet analysis is one of the most important methods for extracting a signal from any data [17][18][19][20]. The Wavelets de-noising application has been widely used.…”

Section: Noise Reduction By Wavelet Threshold Functionmentioning

confidence: 99%

Design and implementation of an automated monitoring system

2016

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“…최근 문서 검색 [1,2], 이미지 매칭 [3], 바이오 시퀀스 매 칭 [4] [14]. 회전-불변 매칭 [15,16]은 이미지가 회전되더라도 동일한 매칭 결과를 얻는 방법으로 [7,9] (2) R1 := RI_Matching(Q, DB, ε); //rotation-invariant m atching using the original query sequence Q.…”

Section: 서 론1 )unclassified

Symmetric-Invariant Boundary Image Matching Based on Time-Series Data

Lee¹,

Bang²,

Moon³

et al. 2015

KIPS Transactions on Software and Data Engineering

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In this paper we address the symmetric-invariant problem in boundary image matching. Supporting symmetric transformation is an important factor in boundary image matching to get more intuitive and more accurate matching results. However, the previous boundary image matching handled rotation transformation only without considering symmetric transformation. In this paper, we propose symmetric-invariant boundary image matching which supports the symmetric transformation as well as the rotation transformation. For this, we define the concept of image symmetry and formally prove that rotation-invariant matching of using a symmetric image always returns the same result for every symmetric angle. For efficient symmetric transformation, we also present how to efficiently extract the symmetric time-series from an image boundary. Finally, we formally prove that our symmetric-invariant matching produces the same result for two approaches: one is using the time-series extracted from the symmetric image; another is using the time-series directly obtained from the original image time-series by symmetric transformation. Experimental results show that the proposed symmetric-invariant boundary image matching obtains more accurate and intuitive results than the previous rotation-invariant boundary image matching. These results mean that our symmetric-invariant solution is an excellent approach that solves the image symmetry problem in time-series domain.

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“…Lindstaedt et al [6] proposed visual folksonomy based automatic image tagging especially for fruits and vegetables. In addition, Manh and Lee [7] focus on small object segmentation based on visual saliency in natural images and Divya et al [8], Santosh and Shyam [9], and Patil and Kokare [10] demonstrated that image segmentation and automatic image tagging models help with semantic image retrieval.…”

Section: Related Researchesmentioning

confidence: 99%

Automatic Image Tagging Model Based on Multigrid Image Segmentation and Object Recognition

Jun

Lee

Jun

2014

Advances in Multimedia

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Since rapid growth of Internet technologies and mobile devices, multimedia data such as images and videos are explosively growing on the Internet. Managing large scale multimedia data with correct tags and annotations is very important task. Incorrect tags and annotations make it hard to manage multimedia data. Accurate tags and annotation ease management of multimedia data and give high quality retrieve results. Fully manual image tagging which is tagged by user will be most accurate tags when the user tags correct information. Nevertheless, most of users do not make effort on task of tagging. Therefore, we suffer from lots of noisy tags. Best solution for accurate image tagging is to tag image automatically. Robust automatic image tagging models are proposed by many researchers and it is still most interesting research field these days. Since there are still lots of limitations in automatic image tagging models, we propose efficient automatic image tagging model using multigrid based image segmentation and feature extraction method. Our model can improve the object descriptions of images and image regions. Our method is tested with Corel dataset and the result showed that our model performance is efficient and effective compared to other models.

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Interactive Semantic Image Retrieval

Cited by 13 publications

References 23 publications

Design and implementation of an automated monitoring system

Design and implementation of an automated monitoring system

Symmetric-Invariant Boundary Image Matching Based on Time-Series Data

Automatic Image Tagging Model Based on Multigrid Image Segmentation and Object Recognition

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