Content-based texture image retrieval using fuzzy class membership

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

doi:10.1016/j.patrec.2013.01.001

Cited by 32 publications

(21 citation statements)

References 30 publications

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“…In some previous works, image retrieval has been performed using image classification [29][30][31][32][33]. These methods extract features from the images in different categories which are then learnt using a classifier.…”

Section: Proposed Model For Object Image Retrievalmentioning

confidence: 99%

An improved radial basis function neural network for object image retrieval

Montazer

Giveki

2015

Neurocomputing

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Section: Proposed Model For Object Image Retrievalmentioning

confidence: 99%

An improved radial basis function neural network for object image retrieval

Montazer

Giveki

2015

Neurocomputing

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“…They are (i) computation of feature set, (ii) computation of class label and fuzzy class membership, (iii) selection of search space and distance metric and (iv) dis(similarity) computation and retrieval. The first two steps are identical as that of CMR method proposed in [16].…”

Section: Proposed Retrieval Methodsmentioning

confidence: 99%

“…The class label and the output fuzzy class membership values are obtained using a trained multilayer feed forward neural network as explained in [16]. The feed forward neural network with one hidden layer is trained with the help of labelled training image data set.…”

Section: Class Label and Fuzzy Class Membership Computationmentioning

confidence: 99%

“…Although CCBR provides better result than simple distance-based retrieval (SDR), it also produces a high variation in the query performance. To overcome the drawback of conventional classifier-based approach Mukhopadhyay et al [16] proposed a technique called 'class membership-based retrieval' (CMR). Their system learns the fuzzy output class membership values of the query image with the help of labelled training data and a neural network.…”

Section: Introductionmentioning

confidence: 99%

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

Dash¹,

Mukhopadhyay²,

Gupta

2015

IET Image Processing

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“…The methods are briefly summarised as follows:(i) Conventional Classifier Based Retrieval (CCBR): This method uses a trained neural network to classify the input query image into one of the output class. Retrieval is performed only from the class suggested by the classifier 6,7. (ii) Classification Confidence-based Retrieval (CCR): This method uses a trained neural network to classify the input query image into one of the output class.…”

mentioning

confidence: 99%

Complementary cumulative precision distribution: a new graphical metric for medical image retrieval system

2014

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Several single valued measures have been proposed by researchers for the quantitative performance evaluation of medical image retrieval systems. Precision and recall are the most common evaluation measures used by researchers. Amongst graphical measures proposed, precision vs. recall graph is the most common evaluation measure. Precision vs. recall graph evaluates different systems by varying the operating points (number of top retrieval considered). However, in real life the operating point for different applications are known. Therefore, it is essential to evaluate different retrieval systems at a particular operating point set by the user. None of the graphical metric provides the variation of performance of query images over the entire database at a particular operating point. This paper proposes a graphical metric called Complementary Cumulative Precision Distribution (CCPD) that evaluates different systems at a particular operating point considering each images in the database for query. The strength of the metric is its ability to represent all these measures pictorially. The proposed metric (CCPD) pictorially represents the different possible values of precision and the fraction of query images at those precision values considering number of top retrievals constant. Different scalar measures are derived from the proposed graphical metric (CCPD) for effective evaluation of retrieval systems. It is also observed that the proposed metric can be used as a tie breaker when the performance of different methods are very close to each other in terms of average precision.

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Content-based texture image retrieval using fuzzy class membership

Cited by 32 publications

References 30 publications

An improved radial basis function neural network for object image retrieval

An improved radial basis function neural network for object image retrieval

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

Complementary cumulative precision distribution: a new graphical metric for medical image retrieval system

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