Content‐based image retrieval using fuzzy class membership and rules based on classifier confidence

Dash, Jatindra Kumar; Mukhopadhyay, Sudipta; Gupta, Rahul

doi:10.1049/iet-ipr.2014.0299

Cited by 17 publications

(2 citation statements)

References 26 publications

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“…Feature extraction and selection is considered to be the first and major step in which an image is analysed in whole or in parts. Many authors proposed feature descriptors that capture colour, texture, and shape of an object within the image [12][13][14]. The similarity measure is considered to be the second step that reduces the semantic gap between the images.…”

Section: Introductionmentioning

confidence: 99%

New strategy for CBIR by combining low‐level visual features with a colour descriptor

Unar

Wang

et al. 2019

IET Image Processing

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In computer vision, the analysis of image contents plays a significant role to perform intelligent tasks such as object recognition and image retrieval. These contents can be low-level visual features or colour information within an image. For content-based image retrieval (CBIR), several methods have been proposed that focus on either low-level visual features extraction or the colour information, and very few works can be seen that retrieve the images by fusing both types of contents. Consequently, this work addresses the problem of combining low-level visual features with colour information that helps to improve the retrieval accuracy of CBIR. The proposed strategy extracts the low-level visual salient features with features from accelerated segment test feature descriptor and quantises the salient keypoints into a feature vector. The colour information of the image is extracted and segmented with non-linear L*a*b* colour space and quantised into a feature vector. The similarity for both the feature vectors including visual and colour features is computed and combined together. The top-rank images are retrieved for the obtained feature vector using the distance metric. The experimental results on two standard benchmark datasets show the improved efficiency and 85% accuracy of the proposed strategy over state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

New strategy for CBIR by combining low‐level visual features with a colour descriptor

Unar

Wang

et al. 2019

IET Image Processing

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“…Hence, an optimal feature set selection will be crucial in improving the performance. A learning-based retrieval system, i.e., class membershipbased retrieval (CMR) [23], [5], helps to overcome the limitation of CCBR by using a fuzzy class membership of the query nodule based on the confidence of the classifier. The CMR uses a weighted distance for high confidence of classifier and uses a simple distance in case of lack of high confidence.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

et al. 2018

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Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured.

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A Comprehensive Review on Content-Based Image Retrieval System: Features and Challenges

Bhatt

Mankodia

2020

Lecture Notes on Data Engineering and Communications Technologies

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Content‐based image retrieval using fuzzy class membership and rules based on classifier confidence

Cited by 17 publications

References 26 publications

New strategy for CBIR by combining low‐level visual features with a colour descriptor

New strategy for CBIR by combining low‐level visual features with a colour descriptor

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

A Comprehensive Review on Content-Based Image Retrieval System: Features and Challenges

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