Automatic Classification for Various Images Collections Using Two Stages Clustering Method

Cho, Wan Hyun; Na, In Seop; Choi, Jun Yong; Lee, Tae Hoon

doi:10.4236/ojapps.2013.31b010

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…To this end, many studies are specific to cluster analysis on image datasets [29], [30], focusing on the object shapes, joints, and localization [31]; colors [1,32]; and texture or image segmentation [1,33] through which image regions are discriminated. Likewise, Karthikeyan [24] studies image clustering using content-based image retrieval (CBIR) on whole images.…”

Section: Cluster Analysismentioning

confidence: 99%

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Kuru

Khan

2018

Applied Soft Computing

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Current widely employed clustering approaches may not yield satisfactory results with regard to the characteristics and distribution of datasets and number of clusters to be sought, especially for visual domains in multidimensional space. This study establishes a novel clustering methodology using a pairwise similarity matrix, Clustering Visual Objects in Pairwise Similarity Matrix (CVOIPSM), for grouping similar objects in specific visual domains. A dimensionality reduction and feature extraction technique, along with a distance measuring method and a newly established algorithm, Clustering in Pairwise Similarity Matrix (CIPSM), are combined to develop the CVOIPSM methodology. CIPSM utilizes both Rk-means and an agglomerative, contractible, expandable (ACE) technique to calculate a membership degree based on maximizing inter-class similarity and minimizing intra-class similarity. CVOIPSM has been tested on several datasets, with average success rates on downsized subsamples between 87.5% and 97.75% and between 81% and 87% on the larger datasets. The difference in the success rates for small and large datasets is not statistically significant (p>0.01). Moreover, this method automatically determines the likely number of clusters without any user dictation. The empirical results and the statistical significance test on these results ensure that CVOIPSM performs effectively and efficiently on specific visual domains, disclosing the interrelated patterns of similarities among objects.

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Section: Cluster Analysismentioning

confidence: 99%

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Kuru

Khan

2018

Applied Soft Computing

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“…D. Choudhary et al 2013, has shown that the wavelet features can be used to generate feature vector [4]. The combination of local and global properties can also be considered to generate feature vectors (W.H.Cho et al 2013) [5]. These extracted features are applied to classifiers such as nearest neighbor classifier(O.…”

Section: Introductionmentioning

confidence: 99%

Generation of an Effective Training Feature Vector using VQ for Classification of Image Database

Kekre¹,

Sarode²,

Save³

2014

IJCA

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In supervised classification of image database, feature vectors of images with known classes, are used for training purpose. Feature vectors are extracted in such a way that it will represent maximum information in minimum elements. Accuracy of classification highly depends on the content of training feature vectors and number of training feature vectors. If the number of training images increases then the performance of classification also improves. But it also leads to more storage space and computation time. The main aim of this research is to reduce the number of feature vectors in an effective way so as to reduce memory space required and computation time as well as to increase an accuracy. This paper proposes three major steps for automatic classification of image database. First step is the generation of feature vector of an image using column transform, row mean vector and fusion method. Then vector Quantization (code book size 4,8 and 16) is applied to reduce the number of training feature vectors per class and generate an effective and compact representation of them. Finally nearest neighbor classification algorithm is used as a classifier. The experiments are conducted on augmented Wang database. The results for various transforms, different similarity measures, varying sizes of feature vector, three code book sizes and different number of training images, are analyzed and compared. Results show that the proposed method increases accuracy in most of the cases.

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Automatic Classification for Various Images Collections Using Two Stages Clustering Method

Cited by 2 publications

References 11 publications

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Generation of an Effective Training Feature Vector using VQ for Classification of Image Database

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