Performance analysis of classification algorithms applied to Caltech101 image database

Ajeesh, S. S.; Indu, M. S.; Sherly, Elizabeth

doi:10.1109/icicict.2014.6781364

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…In the two system, the first system uses global methods to classify skin lesions. The second system uses local features and the bag-of-features classiffier [6].Ajeesh S S and Indu M S evaluate the performance of the selected algorithms for a particular database [5]. Insight for using multiple classifiers was derived from the proposed work.…”

Section: Hina Sood Etal Proposed Segmentation Of Skin Lesions Frommentioning

confidence: 99%

Prescreening of Skin Lesions from Digital Images

2015

IJSR

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Melanoma is one of the deadliest form of skin cancer. Incidence rates of melanoma have been increasing, especially white males and females, but survival rates are high if detected early. The automated systems assess a patient's risk of melanoma using images of their skin lesions which is taken using a standard digital camera. Locating the skin lesion in the digital image is a big challenge. A set of texture distributions were learned from an illumination-corrected photograph. Texture distinctiveness metric is calculated for each texture distribution. Later regions in the image are classified as normal or cancerous based on representative texture distributions. Texture filter based skin lesion segmentation is proposed. Based on this method the region of lesion is segmented without noise. Features are extracted from the segment and fed to classifier. The proposed work has higher segmentation accuracy compared to all other tested algorithms.

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Section: Hina Sood Etal Proposed Segmentation Of Skin Lesions Frommentioning

confidence: 99%

Prescreening of Skin Lesions from Digital Images

2015

IJSR

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“…For example, in [1] In [18], the authors implemented a CBIR for retrieving digital mammograms. They used a two-stage classifier in which the first stage completes a preliminary task to determine whether two images are sufficiently similar for further consideration.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Exploring Mediatoil Imagery: A Content-based Approach

Saroop¹,

Viktor

McCurdy

et al. 2017

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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The future of Alberta's bitumen sands, also known as "oil sands" or "tar sands," and their place in Canada's energy future has become a topic of much public debate. Within this debate, the print, television, and social media campaigns of those who both support and oppose developing the oil sands are particularly visible. As such, campaigns around the oil sands may be seen as influencing audience perceptions of the benefits and drawbacks of oil sands production. There is consequently a need to study the media materials of various tar sands stakeholders and explore how they differ. In this setting, it is essential to gather documents and identify content within images, which requires the use of an image retrieval technique such as a content-based image retrieval (CBIR) system. In a CBIR system, images are represented by low-level features (i.e. specific structures in the image such as points, edges, or objects), which are used to distinguish pictures from one another.The oil sands domain has to date not been mapped using CBIR systems. The research thus focuses on creating an image retrieval system, namely Mediatoil-IR, for exploring documents related to the oil sands. Our aim is to evaluate various low-level representations of the images within this context. To this end, our experimental framework employs LAB color histogram (LAB) and speeded up robust features (SURF) in order to typify the imagery. We further use machine learning techniques to improve the quality of retrieval (in terms of both accuracy and speed). To achieve this aim, the extracted features from each image are encoded in the form of vectors and used as a training set for learning classification models to organize pictures into different categories. Different algorithms were considered such as Linear SVM, Quadratic SVM, Weighted KNN, Decision Trees, Bagging, and Boosting on trees. It was shown that Quadratic SVM algorithm trained on SURF features is a good approach for building CBIR, and is used in building Mediatoil-IR.Finally, with the help of created CBIR, we were able to extract the similar documents and explore the different types of imagery used by different stakeholders. Our experimental evaluation shows that our Mediatoil-IR system is able to accurately explore the imagery used by different stakeholders.iii

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