scholarly journals Deformation and Viewpoint Invariant Color Histograms

2006 ◽  
Author(s):  
J. Domke ◽  
Y. Aloimonos
Keyword(s):  
Color Histograms

Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations

2016 ◽  
Vol 46 ◽  
pp. 753-766 ◽  
Author(s):  
Chia-Feng Juang ◽  
Guo-Cyuan Chen ◽  
Chung-Wei Liang ◽  
Demei Lee
Keyword(s):  
Object Detection ◽  
Fuzzy Classifier ◽  
Stereo Camera ◽  
Color Histograms

VIDEO CONTENT REPRESENTATION FOR SHOT RETRIEVAL AND SCENE EXTRACTION

2001 ◽  
Vol 01 (03) ◽  
pp. 507-526 ◽  
Author(s):  
TONG LIN ◽  
HONG-JIANG ZHANG ◽  
QING-YUN SHI
Keyword(s):  
Similarity Measure ◽  
Video Content ◽  
Object Based ◽  
Scene Structure ◽  
Color Histograms ◽  
Extraction Algorithm ◽  
Content Representation ◽  
Video Shot ◽  
Shot Boundary ◽  
Spatio Temporal

In this paper, we present a novel scheme on video content representation by exploring the spatio-temporal information. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure that serves to shot retrieval. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experimental results on real-world sports video prove that our proposed approach for video shot retrievals achieve the best performance on the average recall (AR) and average normalized modified retrieval rank (ANMRR), and Experiment on MPEG-7 test videos achieves promising results by the proposed scene extraction algorithm.

Using Color Histograms for Shrunk Images Comparison

2021 ◽  
Author(s):  
Yuriy Furgala ◽  
Andriy Velhosh ◽  
Serhiy Velhosh ◽  
Bohdan Rusyn
Keyword(s):  
Color Histograms

Using color histograms and SPA-LDA to classify bacteria

2014 ◽  
Vol 406 (24) ◽  
pp. 5989-5995 ◽  
Author(s):  
Valber Elias de Almeida ◽  
Gean Bezerra da Costa ◽  
David Douglas de Sousa Fernandes ◽  
Paulo Henrique Gonçalves Dias Diniz ◽  
Deysiane Brandão ◽  
...  
Keyword(s):  
Color Histograms

Motor Oil Classification Using Color Histograms and Pattern Recognition Techniques

2018 ◽  
Vol 101 (6) ◽  
pp. 1967-1976 ◽  
Author(s):  
Shiva Ahmadi ◽  
Ahmad Mani-Varnosfaderani ◽  
Biuck Habibi
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Classification Problem ◽  
Classification Model ◽  
Motor Oil ◽  
Pattern Recognition Techniques ◽  
Color Histograms ◽  
Different Types ◽  
Motor Oils

Abstract Motor oil classification is important for quality control and the identification of oil adulteration. In this work, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

Weighted fuzzy correlation for similarity measure of color-histograms

2005 ◽  
Vol 247 (1-3) ◽  
pp. 49-55 ◽  
Author(s):  
Hongchen Zhai ◽  
Pierre Chavel ◽  
Yi Wang ◽  
Siyuan Zhang ◽  
Yanmei Liang
Keyword(s):  
Similarity Measure ◽  
Fuzzy Correlation ◽  
Color Histograms
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