Research Article/Paper http://localhost:8080/xmlui/handle/123456789/978 2026-06-23T06:21:32Z 2026-06-23T06:21:32Z Bhangare, Mayuri A. Mankar, J. R. http://localhost:8080/xmlui/handle/123456789/995 2018-05-30T09:37:37Z 2017-04-01T00:00:00Z

Title: OUTLIER DETECTION USING CLUSTER-BASED APPROACH: A REVIEW Authors: Bhangare, Mayuri A.; Mankar, J. R. Abstract: Outlier detection is a crucial task in data mining which aims to detect an outlier from given data set. The data is said to be an outlier which appears to have inconsistent observation with the remaining data. Outliers are generated because of improper measurements, data entry errors or data arriving from various sources than remaining data. Outlier detection is the technique which discovers such type of data from the given data set. Several techniques of outlier detection have been introduced which requires input parameter from the user such as distance threshold, density threshold, etc. The goal of this proposed work is to partition the input data set into the number of clusters and then outlier is detected for each cluster. The computational time is affected as the data set size is reduced in first phase due to clustering. This work aims at studying two-three different methods of outlier detection with two different data sets. Also analyzing the performance of each method based on system accuracy.

2017-04-01T00:00:00Z Kawale, Sandhya V. Kamlapurkar, S. M. http://localhost:8080/xmlui/handle/123456789/994 2018-05-30T09:21:28Z 2017-04-01T00:00:00Z

Title: A REVIEW ON IMAGE RETRIEVAL USING HYPERGRAPH Authors: Kawale, Sandhya V.; Kamlapurkar, S. M. Abstract: Retrieving images similar to query image from a large image collection is a challenging task. Image consists of different regions. There are several methods in the literature which are useful to capture region level similarities between pair of images using graph. Each image can be represented by several visual concepts. Visual concept is the object or part of the image having some visual information. There are several images in the database which can be sharing the same visual concepts. Graphs are fails to capture the relationship between multiple vertices. Hypergraph is useful to represent group relationship between multiple vertices. Consider database images as a vertices and visual concept as a hyperedge of a hypergraph. All the images sharing same visual concept, form a hyperedge. Ranking methods on these hypergraph is design, to rank all the images in the database which are relevant to the query. Top k images are retrieved from these images which will handle query relevant image retrieval

2017-04-01T00:00:00Z Bhandare, Sonal B. Kamlapurkar, S. M. http://localhost:8080/xmlui/handle/123456789/993 2018-05-30T09:11:17Z 2017-04-01T00:00:00Z

Title: TEXTURE CLASSIFICATION METHODS: A REVIEW Authors: Bhandare, Sonal B.; Kamlapurkar, S. M. Abstract: Texture classification is an important area of re-search in pattern recognition and image processing. It is widely used in real world applications like object detection, face recognition, medical image processing, agriculture etc. Texture has no deterministic shape or specific structure like other natural images. Texture is treated as indication for getting significant knowledge about the texture class. Since last one decade texture classification methods mainly uses patch based local features and feature encoding techniques. So in this paper some representative methods for each of the context are analysed.

2017-04-01T00:00:00Z Narkhede, Dhanshree S. Mankar, J. R. http://localhost:8080/xmlui/handle/123456789/992 2018-05-30T09:04:31Z 2017-03-01T00:00:00Z

Title: A Review on Label Prediction through Multiple Visual Features Authors: Narkhede, Dhanshree S.; Mankar, J. R. Abstract: Multiple visual features are represented by multimedia data. Multi-feature learning aims at using the complementary structural information of visual features. The focus is on the semi-supervised learning when the label information of the training data is insufficient. Most of the existing systems face the problem of insufficient labelled data that are expensive to label by hand in real-world application. To address this problem classifier has been already proposed in the literature that select features closely similar to the query image and based on these features label prediction is done. This work aims at studying different low-level feature descriptor for better feature extraction and focusing on computational time of the system by replacing SIFT descriptor by ORB feature descriptor

2017-03-01T00:00:00Z