Mrs Poornima Raikar
@klsvdit.edu.in
Assistant Professor
KlS VDIT Haliyal
RESEARCH INTERESTS
image processing
Scopus Publications
Scopus Publications
Poornima Raikar and S. M. Joshi
Springer Nature Singapore
Poornima Raikar and S.M Joshi
IEEE
The field of machine learning is growing in modern times, computational models are able to go beyond the performance of previous forms of artificial intelligence. The use of evaluation model ,selection of model and algorithm selecting techniques play an major role in machine learning study and also in field of industries. In this work, we made evaluation of various supervised, unsupervised machine learning classifiers for flower datasets. We made use of local features such as Histogram of gradient , Kaze, Local binary pattern(LBP) ,Oriented Fast and Rotated Brief( ORB), global features like Color Histograms, Haralick Textures , Hu Moments , fusion of both and Bag of visual words(BOVW) using Vocabulary builder K-Means clustering which represents color ,texture, shape features of image. Experiment is carried out on 20 classes of flower datasets with 100 images each. .Flower datasets have many characteristic in common like sunflower will be similar to daffodil in terms of color and texture .Hence to quantify the image we need to combine different feature descriptors like color, texture and shape features. We develop a Content based classification system to find efficiency comparison of different machine learning algorithms for classification and retrieval problems. Eleven classifiers mainly Support Vector Machine, K Nearest Neighbor, Gaussian Naive Bayes , CART, Kmeans, Linear Discriminant Analysis, Adaboost ,Logistic Regression, MLP, Random Forest, CNN are analyzed on the shape, color ,texture features. Experimentation are carried out and results are recorded using CPU as well as GPU on google cobalatory platform.
Poornima Raikar
The World Academy of Research in Science and Engineering
51 ABSTRACT Content based image retrieval (CBIR) provides efficient way to retrieve the images from the databases. The feature extraction and similar attribute measures are the key factors for retrieval performance. We need efficient way to access the visual content from large database. Content based image retrieval (CBIR) provides the solution for efficient retrieval of images from large image database. In this work hybrid feature based CBIR system is proposed with comparison of various distance measures. Spatial features like color histogram, color auto-correlogram, color moments, HSV histogram features and Frequency domain features like Semantic image features Gabor wavelet mean entropy, amplitude, energy. Wavelet moments like mean and the standard deviation of the transform coefficients, Shape feature histogram of oriented gradient , Hu moments are used to form the feature vector. The experiments are performed on flower database which consists of 1360 images from 17 different classes. For our experiment we have chosen 3 different classes of flowers of same color consisting of 100 images each. Experimental result shows that the proposed approach performs better in terms of precision, recall, accuracy of classifier and similarity measures .Shape feature play an prominent role for images with same color, texture. We made the comparison of efficiency of different similarity measures like Mahalanobis , Euclidean, Correlation, Spearman, City block(Manhattan) distances approaches on different images based on color, texture, shape features and found which distance measure is best based on performance.