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In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

Watershed transform is a commonly used image segmentation method. The main problem with this segmentation technique is that of its sensitivity to noise and other irregularities which leads to over-segmentation. In this paper the over-segmentation problem is overcome by combing pre-processing and post-processing techniques along with watershed transform. First multi-scale morphological filtering by reconstruction is used to remove noise and then h minima transform is implemented to extract markers. These markers are then superimposed on gradient image. Watershed transform is then applied on the modified gradient map. Postprocessing region merging technique is used to merge the over segmented regions in the final segmented map. Experimental results show that the over-segmentation problem is reduced with the average segmentation accuracy of 0.96.