Issue in recognising multiple objects in an Image

Question

Rohan Gupta 2017-1-21

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评论： william pyae 2018-5-26

I have images of Apples as well as of Oranges, which I am using as training images. The test image is an image consisting of both apple and orange. I am using GIST descriptor for feature extraction. When I train the classifier using extracted features, it gives an output as apple or orange for the test image. I have a query, as how can I make classifier recognise both of them in the test image. I am using KNN classifier

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Rohan Gupta 2017-1-23

在 MATLAB Online 中打开

Images.zip

Hello Sir, I have attached the folder consisting of some of the train images and one test image. The knn classifier classifies the test image as either orange or apple, I am really struggling to find a way to make classifier identify both orange and apple in an image. It would be really grateful, if you can help me. Given below is my code.

clc; clear all;

imfile = dir('C:\Users\JTS_10\Desktop\Fruits\train_imag\train\*.jpg'); imfile1 = dir('C:\Users\JTS_10\Desktop\Fruits\test_imag\test\*.jpg');

a = struct2cell(imfile); train_lists = {a(1,:)};

d = struct2cell(imfile1); test_lists = {d(1,:)};

g = zeros(length(imfile),512);

l = {'Apple';'Orange'}; train_label = [repmat(l(1),10,1);repmat(l(2),10,1)]; test_label = [repmat(l(1),2,1);repmat(l(2),2,1)];

%% Extract features from Train Images

for i = 1:length(imfile) img = strcat('C:\Users\JTS_10\Desktop\Fruits\train_imag\train\',imfile(i).name); I1 = imread(img);

    % Parameters:
    param.imageSize = [256 256]; % it works also with non-square images
    param.orientationsPerScale = [8 8 8 8];
    param.numberBlocks = 4;
    param.fc_prefilt = 4;
    [gist1, param] = LMgist(I1, '', param);
    g(i,:) = gist1;
end

%% Extract features from Test Image

    img1 = strcat('C:\Users\JTS_10\Desktop\Fruits\test_imag\test\',imfile1(i).name);
    I1 = imread(img1);
    imshow(I1);
    % Parameters:
    param.imageSize = [256 256]; % it works also with non-square images
    param.orientationsPerScale = [8 8 8 8];
    param.numberBlocks = 4;
    param.fc_prefilt = 4;
    [gist2, param] = LMgist(I1, '', param);

class = knnclassify(gist2,g,train_label);

Rohan Gupta 2017-1-25

It would be really helpful if someone would help me out with this problem. I would like to know if my approach is correct or not.

william pyae 2018-5-26

Hi Rohan, I'm doing a similar project as yours. Could you able to post all your matlab code in the file exchange? I would like to take references from your project. Thank you so much.

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Answer 1

Image Analyst 2017-1-25

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Why not simply look at the color? Just convert to HSV color space, mask out the background and look at the amount of orange in the image. If there's more orange than non-orange, it's an orange.

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Image Analyst 2017-1-27

在 MATLAB Online 中打开

regionprops() will tell you the hue of every single region in the image. Once you've made a determination, you can assign a string with the name of the fruit. Like

props = regionprops(binaryImage, hueImage, 'MeanIntensity');
for k = 1 : length(props)
    thisHue = props(k).MeanIntensity
    if thisHue < 0.1 % or whatever
        fruitType{k} = 'Apple'
    else
        fruitType{k} = 'Orange'
    end
end

Rohan Gupta 2017-1-28

Thank-You Sir, it works

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Answer 2

Takuji Fukumoto 2017-1-25

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https://ww2.mathworks.cn/matlabcentral/answers/321355-issue-in-recognising-multiple-objects-in-an-image#answer_251930

Why don't you use R-CNN? http://jp.mathworks.com/help/vision/ref/trainrcnnobjectdetector.html

I think you should cut block from a whole image and slide it for recognition if you want to use that classification.

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Takuji Fukumoto 2017-1-25

编辑：Takuji Fukumoto 2017-1-25

I mean it can work if you create 'search window'. The search window is used in some detector algorithm.

https://www.mathworks.com/help/releases/R2016b/vision/ref/vision.peopledetector-class.html

RCNN find something like object first and then use classifier.

Rohan Gupta 2017-1-27

I am not suppose to use RCNN or CNN for this application. Is there any other way. By the way thank you for your valuable inputs. It would be really helpful, if you could help me out further.

The single test image will have 3 or 4 fruits, How can I detect all of them?

Features of each fruits can be extracted using color as it's feature (during training), but when I train KNN classifier and give it a test image, it will give only one output (i.e orange or apple). It won't tell me the names of all the fruits present in the image.

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