A Cascaded Retrieval Method of Specified Object Based on Fusing Multiple Features

2012 ◽  
Author(s):  
Jinyong Wu ◽  
Yong Zhao ◽  
Xing Zhang ◽  
Jun Wang ◽  
Yike Wang
Keyword(s):  
Multiple Features ◽  
Retrieval Method ◽  
Object Based

A New Image Retrieval Method Based on K-Nearest Neighbor Multistage and Multiple Features

2014 ◽  
Vol 12 (3) ◽  
pp. 479-484 ◽  
Author(s):  
Jun Yue ◽  
Yupeng Wang ◽  
Zhenbo Li ◽  
Zhiwang Zhang ◽  
Jialin Hou
Keyword(s):  
Image Retrieval ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Multiple Features ◽  
Retrieval Method

RETRIEVING IMAGES BY COMPARING HOMOGENEOUS COLOR AND TEXTURE OBJECTS IN THE IMAGE

2004 ◽  
Vol 18 (06) ◽  
pp. 1093-1110 ◽  
Author(s):  
HUN-WOO YOO ◽  
DONG-SIK JANG ◽  
KWANG-KYU SEO ◽  
MYUNG-EUI LEE
Keyword(s):  
Image Segmentation ◽  
Image Retrieval ◽  
Vector Quantization ◽  
Textural Features ◽  
Clustering Method ◽  
Retrieval Method ◽  
Simple Representation ◽  
Object Based ◽  
Efficient Retrieval ◽  
Image Segmentation Algorithm

An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

Towards a Validation of Multiple Features in the Assessment of Emotions

1998 ◽  
Vol 14 (3) ◽  
pp. 202-210 ◽  
Author(s):  
Suzanne Skiffington ◽  
Ephrem Fernandez ◽  
Ken McFarland
Keyword(s):  
Factor Analysis ◽  
Pilot Study ◽  
Cognitive Appraisal ◽  
Negative Emotions ◽  
The Self ◽  
Factor Analyses ◽  
Data Set ◽  
Multiple Features ◽  
Action Tendency ◽  
Intensity Range

This study extends previous attempts to assess emotion with single adjective descriptors, by examining semantic as well as cognitive, motivational, and intensity features of emotions. The focus was on seven negative emotions common to several emotion typologies: anger, fear, sadness, shame, pity, jealousy, and contempt. For each of these emotions, seven items were generated corresponding to cognitive appraisal about the self, cognitive appraisal about the environment, action tendency, action fantasy, synonym, antonym, and intensity range of the emotion, respectively. A pilot study established that 48 of the 49 items were linked predominantly to the specific emotions as predicted. The main data set comprising 700 subjects' ratings of relatedness between items and emotions was subjected to a series of factor analyses, which revealed that 44 of the 49 items loaded on the emotion constructs as predicted. A final factor analysis of these items uncovered seven factors accounting for 39% of the variance. These emergent factors corresponded to the hypothesized emotion constructs, with the exception of anger and fear, which were somewhat confounded. These findings lay the groundwork for the construction of an instrument to assess emotions multicomponentially.

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