In this paper, a novel approach for automatic segmentation and classification
of skin lesions is proposed. Initially, skin images are filtered to remove
unwanted hairs and noise and then the segmentation process is carried out to
extract lesion areas. For segmentation, a region growing method is applied by
automatic initialization of seed points. The segmentation performance is
measured with different well known measures and the results are appreciable.
Subsequently, the extracted lesion areas are represented by color and texture
features. SVM and k-NN classifiers are used along with their fusion for the
classification using the extracted features. The performance of the system is
tested on our own dataset of 726 samples from 141 images consisting of 5
different classes of diseases. The results are very promising with 46.71% and
34% of F-measure using SVM and k-NN classifier respectively and with 61% of
F-measure for fusion of SVM and k-NN.Comment: 10 pages, 6 figures, 2 Tables in Elsevier, Proceedia Computer
Science, International Conference on Advanced Computing Technologies and
Applications (ICACTA-2015
In this paper, we introduce a new measure called Term_Class relevance to
compute the relevancy of a term in classifying a document into a particular
class. The proposed measure estimates the degree of relevance of a given term,
in placing an unlabeled document to be a member of a known class, as a product
of Class_Term weight and Class_Term density; where the Class_Term weight is the
ratio of the number of documents of the class containing the term to the total
number of documents containing the term and the Class_Term density is the
relative density of occurrence of the term in the class to the total occurrence
of the term in the entire population. Unlike the other existing term weighting
schemes such as TF-IDF and its variants, the proposed relevance measure takes
into account the degree of relative participation of the term across all
documents of the class to the entire population. To demonstrate the
significance of the proposed measure experimentation has been conducted on the
20 Newsgroups dataset. Further, the superiority of the novel measure is brought
out through a comparative analysis.Comment: 12 pages, 6 figures, 2 table
Abstract. This paper presents a novel approach for video shot boundary detection. The proposed approach is based on split and merge concept. A fisher linear discriminant criterion is used to guide the process of both splitting and merging. For the purpose of capturing the between class and within class scatter we employ 2D 2 FLD method which works on texture feature of regions in each frame of a video. Further to reduce the complexity of the process we propose to employ spectral clustering to group related regions together to a single there by achieving reduction in dimension. The proposed method is experimentally also validated on a cricket video. It is revealed that shots obtained by the proposed approach are highly cohesive and loosely coupled.
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