scholarly journals Image Clustering Using Multi-visual Features

2014 ◽  
pp. 179-189 ◽  
Author(s):  
Bilih Priyogi ◽  
Nungki Selviandro ◽  
Zainal A. Hasibuan ◽  
Mubarik Ahmad
Keyword(s):  
Image Clustering ◽  
Visual Features

scholarly journals Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 3892-3895
Author(s):  
B. K. Alsaidi ◽  
B. J. Al-Khafaji ◽  
S. A. A. Wahab
Keyword(s):  
Genetic Algorithm ◽  
Standard Deviation ◽  
Image Data ◽  
One Dimension ◽  
Image Clustering ◽  
Visual Features ◽  
Statistical Features ◽  
Textual Information ◽  
Clustering Technique ◽  
Web Images

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering. The extraction of features gave a high distinguishability and helped GA reach the solution more accurately and faster.

Spam Image Clustering for Identifying Common Sources of Unsolicited Emails

2011 ◽  
pp. 87-103
Author(s):  
Chengcui Zhang ◽  
Xin Chen ◽  
Wei-Bang Chen ◽  
Lin Yang ◽  
Gary Warner
Keyword(s):  
Data Mining ◽  
Prior Knowledge ◽  
Clustering Algorithm ◽  
Visual Similarity ◽  
Image Clustering ◽  
Visual Features ◽  
Common Source ◽  
Clustering Approach ◽  
The Common ◽  
Initial Results

In this article, we propose a spam image clustering approach that uses data mining techniques to study the image attachments of spam emails with the goal to help the investigation of spam clusters or phishing groups. Spam images are first modeled based on their visual features. In particular, the foreground text layout, foreground picture illustrations and background textures are analyzed. After the visual features are extracted from spam images, we use an unsupervised clustering algorithm to group visually similar spam images into clusters. The clustering results are evaluated by visual validation since there is no prior knowledge as to the actual sources of spam images. Our initial results show that the proposed approach is effective in identifying the visual similarity between spam images and thus can provide important indications of the common source of spam images.

An Efficient Graph-Based Flickr Photo Clustering Algorithm

2010 ◽  
Vol 29-32 ◽  
pp. 2649-2655
Author(s):  
Zheng Liu ◽  
Hua Yan ◽  
Zhen Li
Keyword(s):  
Clustering Algorithm ◽  
Image Clustering ◽  
Visual Features ◽  
Edge Weight ◽  
Clustering Methods ◽  
Data Types ◽  
Simultaneous Integration ◽  
Web Images ◽  
Photo Clustering ◽  
Traditional Image

Traditional image clustering methods mainly depends on visual features only. Due to the well-known “semantic gap”, visual features can hardly describe the semantics of the images independently. In the case of Web images, apart from visual features, there are rich metadata which could enhance the performance of image clustering, such as time information, GPS coordinate and initial annotations. This paper proposes an efficient Flickr photo clustering algorithm by simultaneous integration information of multiple types which are related to Flickr photos using k-partite graph partitioning. For a personal collection of Flickr, we firstly determine the value of k which means the number of data types we used. Secondly, these heterogeneous metadata are mapped to vertices of a k-partite graph, and relationship between the heterogeneous metadata is represented as edge weight. Finally, Flickr photos could be clustered by partitioning the k-partite graph. Experiments conducted on the photos in Flickr demonstrate the effectiveness of the proposed algorithm.

Reduced change blindness for task-relevant visual features during a social interaction

2001 ◽  
Author(s):  
Donald A. Varakin ◽  
Sheena Rogers ◽  
Jeffrey T. Andre ◽  
Susan L. Davis
Keyword(s):  
Social Interaction ◽  
Change Blindness ◽  
Visual Features

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

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