Projects:2016s2-246 Feral Cat Detector - Revision history

A1692859: /* Approach */

2017-06-05T04:35:33Z

‎Approach

2017-06-05T04:34:34Z

‎Practical results display

2017-06-05T04:33:48Z

‎Practical results display

2017-06-05T04:32:27Z

‎Practical results display

2017-06-05T04:29:10Z

‎Reference

2017-06-05T04:28:46Z

2017-06-05T04:26:12Z

‎Acknowledge=

2017-06-05T04:25:50Z

2017-06-05T04:24:08Z

‎Practical results display

2017-06-05T04:23:43Z

‎Practical results display

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 <div align="center">Figure 1 Project flow diagram </div>
-Based on the project of Richard & Yang in the last year, we build a alternative classification system which consist of two parts: feature selection and classification. In this project, instead of single feature, we have used feature combination which is proven could improve the classification performance. Moreover, we have test several classifiers to find which one is better and applied the better one in our system.
+Based on the project of Richard & Yang in the last year [1][2], we build a alternative classification system which consist of two parts: feature selection and classification. In this project, instead of single feature, we have used feature combination which is proven could improve the classification performance. Moreover, we have test several classifiers to find which one is better and applied the better one in our system.
 In feature selection, we select 5 single features as base features: SIFT, PHOW, PHOG, LBP and SSIM. At first, we compare the performances of feature combination and single feature to find which way is better. Then, based on 5 base features to test all feature combinations and find the feature which can obtain the highest accuracy.
 For the part of classifictoin, we verify the framework of AdaBoost can improve classifier’s performance firstly, and then find the best classifier among 4 classifiers, SVM, MKL-SVM, softmax, SVM+AdaBoost.

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 Above figure shows one of classification results of PHOW+SIFT, we find the number of correct prediction of cat is 22 with 195 cat test images in total and most cat test images are classified as bird, human and sheep.
-Moreover, we show a practical classification result at below. There are
+Moreover, we show a practical classification result at below. There are 25 images with predicted class.
 <div align="center">[[File:Wiki.jpg|1000px]]</div>

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 ===Practical results display===
 <div align="center">[[File:Phow+sift.jpg|600px]]</div>
 <div align="center">Figure 4 Classification result of phow+sift </div>
 <div align="center">[[File:Wiki.jpg|1000px]]</div>

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 ===Practical results display===
 <div align="center">[[File:Wiki.jpg|1000px]]</div>
-<div align="center">Figure 4 Practical classification results </div>
+<div align="center">Figure 5 Practical classification results </div>
 =='''Acknowledge'''==

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 =='''Reference'''==
 [1]R. Steenvoorde, "Feral Cat Detector", master, The University of Adelaide, 2016.
 [2]S. Yang, "Feral Cat Detector", master, The University of Adelaide, 2016.
 [3]Vedaldi. Andrea, Varun. Gulshan, Manik. Varma, and Andrew. Zisserman, Multiple kernels for object detection, In Computer Vision, 2009 IEEE 12th International Conference on, pp. 606-613. IEEE, 2009.

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 <div align="center">[[File:Feature extraction.jpg|300px]]</div>
-<div align="center">Figure 2 Feature extraction </div>
+<div align="center">Figure 2 Feature extraction [3]</div>
 ===Classification===
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 MKL-SVM is a cascaded SVM which is consisted of three internal SVM classifiers, i.e., fast linear SVM, quasi-linear SVM and non-linear SVM. Each classifier has its distinct kernel. The task of MKL-SVM is to compute kernel matrices of training and test images. Even though the histograms can represent the images, it still impossible to build a linear classifier for every class. Thus, we use a feature map in which the regions of interest (features) are linearly separable to help us find the optimal kernel.
 <div align="center">[[File:Mkl-svm.jpg|500px]]</div>
-<div align="center">Figure 3 Visual object detection via a MKL-SVM </div>
+<div align="center">Figure 3 Visual object detection via a MKL-SVM [3]</div>
 =='''Results analysis'''==
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 For Richard Steenvoorde and Song Yang, thanks for your significantly important image dataset and effective system which inspires and helps us avoid hundreds of crooked roads.

← Older revision		Revision as of 04:25, 5 June 2017
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	<div align="center">Figure 4 Practical classification results </div>		<div align="center">Figure 4 Practical classification results </div>
		+
		+	=='''Acknowledge'''===
		+	For Dr Danny Gibbins & Dr Said Al-Sarawi, thanks for your guidance, advice and support.
		+
		+	For Richard Steenvoorde and Song Yang, thanks for your significantly important image dataset and effective system which inspires and helps us avoid hundreds of crooked roads.