Reduction of semantic gap using relevance feedback technique in image retrieval system

Saju, Ansa; Mary, I. Thusnavis Bella; Vasuki, A.; Lakshmi, P.

doi:10.1109/icadiwt.2014.6814677

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…As a shared intermediate semantic layer, semantic embedding space bridges the semantic gap [25][26][27] between the underlying image feature space and the high-level semantic space, transcends the class boundaries between mutually exclusive objects, which is the key to solve the zero-shot learning problem. For zero-shot learning, semantic embedding spaces [28,29] are usually constructed independently of visual recognition tasks, that is an object class label, we represent an independent label as an interrelated label embedding vector by means of knowledge that is relatively easy to obtain in other fields.…”

Section: Construction Of Semantic Embedding Spacementioning

confidence: 99%

Wheel Hub Defects Image Recognition Base on Zero-Shot Learning

Sun

Wang

et al. 2021

Applied Sciences

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In the wheel hub industry, the quality control of the product surface determines the subsequent processing, which can be realized through the hub defect image recognition based on deep learning. Although the existing methods based on deep learning have reached the level of human beings, they rely on large-scale training sets, however, these models are completely unable to cope with the situation without samples. Therefore, in this paper, a generalized zero-shot learning framework for hub defect image recognition was built. First, a reverse mapping strategy was adopted to reduce the hubness problem, then a domain adaptation measure was employed to alleviate the projection domain shift problem, and finally, a scaling calibration strategy was used to avoid the recognition preference of seen defects. The proposed model was validated using two data sets, VOC2007 and the self-built hub defect data set, and the results showed that the method performed better than the current popular methods.

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Section: Construction Of Semantic Embedding Spacementioning

confidence: 99%

Wheel Hub Defects Image Recognition Base on Zero-Shot Learning

Sun

Wang

et al. 2021

Applied Sciences

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“…Patil [58] also revealed that to define RF technique and scheme needs to take into account certain assumptions and criteria. Saju, A., et al [20] revealed that one way to raise the accuracy of the CBIR system can be done by using RF. But Saju and his team revealed that the biggest challenge to the CBIR system with RF techniques is the number of iterations and execution times required by the system.…”

Section: Support Vector Machine Relevance Feedback (Svm Rf)mentioning

confidence: 99%

“…Semantic gaps can be reduced by involving humans as part of the feedback process, called Relevance Feedback (RF) [1], [20]- [25]. In the CBIR system with RF, it will encourage the user to provide an assessment of the images that retrieved.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms implemented in the CBIR system with RF must also be able to learn and understand users' information needs based on user feedback [23], [26]. The feedback image is rated as positive or negative, highly relevant, relevant or irrelevant, partially relevant, and highly irrelevant [20], [22]. The users' feedback image is used for query improvements or the establishment of a classification model.…”

Section: Introductionmentioning

confidence: 99%

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SVM Relevance Feedback in HSV Quantization for CBIR

Pardede¹

2018

JCP

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In this research have implemented SVM relevance feedback technique in HSV quantization for CBIR. The proposed technique has two stages. The first stage performs image retrieval process based on results of distance measurement. The distance measurement used is Jeffrey Divergence with threshold 0.15. The second stage is image retrieval process based on SVM RF prediction model. The SVM RF model is formed based on user-provided feedback images. The users' feedback images are labeled as positive and others are negative. The purpose of this study is to determine the best value of the constant C on the linear kernel and the best value of the constant (C, G) on the RBF kernel. The best value of the constants provided generates the best model of SVM RF in the HSV Quantization method so that improve the performance of the CBIR system. Performance measurements are evaluated based on precision, recall, F-measure, and accuracy values. Based on the results of experiments that conduct on Wang dataset obtained that (C, G) = (2 2.725 , 2 2.725 ) is the best value on the RBF kernel. While C = 2 5.275 is the best value on SVM RF using linear kernel. The best of SVM RF technique is SVM RF using RBF kernel of second feedback. The SVM RF using RBF kernel increases the average precision value by 3.02%, the average recall value increasing amount 171.48%, the average F-Measure value increasing amount 80.34%, while the average accuracy value increasing amount 1.99% compared with the baseline. The SVM RF using RBF kernel obtains the best value on both the average recall value and the average F-Measure value compared to the state-of-the-art. and 296-315), 3 Saturations, step (0-0.2, 0.2-0.7 and 0.7-1.0) and 3 Values, step (0-0.2, 0.2-0.7 and 0.7-1.0), and with the same principle done for other schema. So for HSV Quantization (19,4,5) will produce 19*4*5 = 380 the number of distinct colors, whereas HSV Quantization (8,3,3) will produce 8*3*3 = 72 the number of distinct colors, and other HSV Quantization schemes.

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“…The first level comprising of feature extraction using local binary pattern (LBP), HSV histogram (HSVH) and color coherence vector (CCV) [4]. The retrieval results using this proposed methodology are also compared with another feature extraction method [5]. It was marked that our approach for primitive feature extraction [4] proves to be better than other approaches.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Approach for Retrieval of Semantically Significant Images

Mane¹,

Rathi²,

Bawane³

2016

IJIGSP

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Abstract-Content-based image retrieval is the process of recovering the images that are based on their primitive features such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between lowlevel primitive features and high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision 73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent features is introduced. On the application of the relevance feedback, only prominent features which are having maximum similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically meaningful relevant images as we go on increasing the iterations.Index Terms-Semantic gap, content-based image retrieval, relevance feedback, HSV histogram, local binary pattern, color coherence vector.

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Reduction of semantic gap using relevance feedback technique in image retrieval system

Cited by 8 publications

References 20 publications

Wheel Hub Defects Image Recognition Base on Zero-Shot Learning

Wheel Hub Defects Image Recognition Base on Zero-Shot Learning

SVM Relevance Feedback in HSV Quantization for CBIR

An Interactive Approach for Retrieval of Semantically Significant Images

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