Object classes. recall (%)

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1 Using Genetic Algorithms to Improve the Accuracy of Object Detection Victor Ciesielski and Mengjie Zhang Department of Computer Science, Royal Melbourne Institute of Technology GPO Box 2476V, Melbourne Victoria 3001, Australia Abstract We describe a two phase approach to the use of pixel based neural networks for object detection problems in which the locations of relatively small objects in large pictures must be found. The networks use a square input eld which is large enough to contain all objects of interest. In the rst phase the network is trained on examples which have been cut out from the large pictures. A genetic algorithm is used for training. The tness function is based on the classication accuracy of the cut-outs. The trained network is then applied, in moving window fashion, over the large pictures to locate the objects of interest. In the second phase the weights of the trained network are adjusted using a second genetic algorithm. The tness function in this case is based on the precision and recall performance of the network on the full training images. We have tested the method on three object detection problems of increasing diculty. In all cases the approach resulted in improved detection performance over networks trained by backward error propagation. The second phase is particularly eective in improving performance in images with cluttered backgrounds. Keywords: Pixel based neural network; Target recognition; Target detection. 1 Introduction As more and more images are captured in electronic form the need for programs which can nd objects of interest in a database of images is increasing. For example, it may be necessary to nd all tumors in a database of x-ray images, all cyclones in a database of satellite images or a particular face in a database of photographs. The common characteristic of such problems can be phrased as \Given subpicture 1 ; subpicture 2 :::subpicturen which are examples of the object of interest, nd all pictures which contain this object and the locations of all of the objects of interest". Figure 1c shows an example of a problem of this kind. The gure shows a human retina. We are required to nd all of the micro aneurisms and haemorrhages, as indicated by the white squares. (Note: The picture is presented at too coarse a level of resolution for the dierence between micro aneurisms and haemorrhages to be evident). We have used a pixel based approach [1, 3, 5], using neural networks, in which the pixel values are used directly as inputs. The paper suggests an improvement to a current approach and describes an investigation of its eect on pictures of increasing diculty. 1

2 1.1 The Basic Object Detection System A brief outline of our basic approach to object detection is as follows [6]: 1. Assemble a database of pictures in which the locations and classes of all of the objects of interest are manually determined. Reserve some of the pictures as `unknowns' for measuring detection performance. 2. Determine an appropriate size (n) of a square which will cover all objects of interest and form the input eld of the networks. 3. Build training and test sets by cutting out squares of size n. The n n pixel values form the inputs of a training pattern and the classication is the output. 4. Choose a hidden layer size and train a three layer feed forward network by backward error propagation. 5. Use the trained network as a moving window template across the full pictures from which the training data was extracted. If the correlation (i.e neural network output) between the image and the template is high enough at any point, an occurrence of the object should be registered at that point. Determine thresholds for each class. 6. Use the trained network as a moving window template on the pictures reserved with step 1. If the output for a class exceeds the threshold then report an object of that type at the current location. 7. Determine the recall, that is, the number of objects correctly reported as a proportion of the total number of objects, and the precision, that is, the number of objects correctly found as a proportion of the total number of objects reported. 1.2 Goals We investigate whether the retrieval performance of the above algorithm can be improved by: 1. Using a genetic algorithm to train the networks; 2. Using a second genetic algorithm, with a tness based on precision and recall on the full training images, to rene the weights of networks obtained in (1). 2 Genetic Algorithm for Network Training The rst phase of our approach requires generating a number of networks trained on cut-outs of the objects of interest. We use the 2DELTA-GANN algorithm [2, 4]. 3 Genetic Algorithm for Network Renement The second phase of our approach involves improving the networks found in the rst phase. This is done by another genetic algorithm which utilizes the same encoding of chromosomes but a dierent tness function. The population for this genetic algorithm is initialized with the best chromosomes available at the end of the rst phase. The tness of a chromosome is calculated as follows: 1. Realize the network from the weights encoded in the chromosome.

2. Apply the network as a moving n n window template across the full training images and get the locations of all objects detected. 3.

recall) (1) where A and B are constants which reect the relative importance of precision vs recall. 4 The Image Databases We used three dierent databases in the experiments.

Database 1 (Easy) was generated to give well dened objects against a uniform background.

3 2. Apply the network as a moving n n window template across the full training images and get the locations of all objects detected. 3. Compare with known locations and determine the precision and recall of this network 4. Compute tness as: f itness = A (1? precision) + B (1? recall) (1) where A and B are constants which reect the relative importance of precision vs recall. 4 The Image Databases We used three dierent databases in the experiments. Example pictures and key characteristics are given in Figure 1. The pictures were selected to provide problems of increasing diculty. Database 1 (Easy) was generated to give well dened objects against a uniform background. The pixels of the objects were generated using a Gaussian generator with dierent means and variances for each class. The coin pictures were intended to be somewhat harder and were taken with a CCD camera over a number of days with relatively similar illumination. In these pictures the background varies slightly in dierent areas of the image and between images and the objects to be detected are more complex, but still regular. The retina pictures were taken by a professional photographer with special apparatus at a clinic and contain irregular objects on a very cluttered background. Note that in each of the databases the background counts as a class. Number of Images: 7 Number of Images: 20 Number of Images: 15 Number of Classes: 4 Number of Classes: 5 Number of Classes: 5 Max size of Object: 12 pixels Max size of Object: 22 pixels Max size of Object: 16 pixels Number of Objects: 240 Number of Objects: 600 Number of Objects: 164 Picture Size 700x700 Picture Size 640x480 Picture Size 1024x1024 Easy (Circles and Squares) Medium Diculty (Coins) Very Dicult (Retinas) (a) (b) (c) Figure 1: Object Detection Problems of Increasing Diculty 5 Results This section describes a series of comparisons between the object detection performance between the basic approach and the new approach. For the easy and coin databases the averages of 10

4 runs are presented. For the retina pictures, due to the high computational cost, the averages of 4 are presented. 5.1 Easy Pictures Table 1 shows the comparison for the easy pictures. The row labelled `Recall' shows what happens as the detection threshold is raised. Setting the threshold to a value which results in all objects in class2 (grey squares) being located results in a large number of `hallucinations', that is, objects claimed to be class2 objects when in fact they are something else and resulting in a precision of 52.31%. If the threshold is raised slightly, a few class2 objects are missed, but there are fewer `hallucinations'. At the point where there are no hallucinations the recall is only 57.67%. This is a common behaviour in detection systems of this kind and systems are compared by looking at the precision at various levels of recall. For objects in class1 (black circles)and class3 (white circles) both the basic approach and the GA approach gave thresholds which resulted in nding all objects of these classes (recall 100%) without any hallucinations (precision 100%). Unlike the basic approach, the GA approach always gave a threshold which resulted in 100% precision and 100% recall for class2. Object classes class1 class3 class2 recall (%) <= best precision (%) Basic Approach With GA Table 1: Comparison of object detection in easy pictures using the basic approach and the GA approach 5.2 Coin Pictures Experiments with the coin images gave similar results. These are shown in Table 2. Detecting heads and tails of the 5 cent coin and tails of the 20 cent coin turned out to be relatively straight forward, while detecting the heads of the 20 cent coins was a dicult problem, as shown in Table 2. However the GA approach achieved 100% precision and recall. 5.3 Retina Pictures The results for the retina pictures are summarized Table 3. Compared to the results for the other two image databases these results are disappointing. However, the GA approach is clearly superior to the basic approach.

5 Object classes head005 tail005 tail020 recall (%) <= best precision (%) Basic Approach With GA Object classes head020 recall (%) best precision (%) Basic Approach With GA Table 2: Comparison of object detection in coin pictures using the basic approach and the GA approach 6 Conclusions The goal of the work described in this paper was to investigate the improvements in retrieval performance of a genetic algorithm based approach over a basic approach using neural networks on the problem of nding small objects in large pictures. In the basic approach the networks are trained by backward error propagation on cut-outs of the objects of interest. The GA approach is a two phase one. In phase one of the approach neural networks are trained on cut-outs of the objects of interest using a genetic algorithm. In the phase two the best networks found in the phase one are rened using a second genetic algorithm which uses precision and recall on a training set of full images as the tness function. Our results show that, for the three detection problems investigated, the new method, is superior to the basic approach. The methods were compared on three detection problems of increasing diculty. On the easy (circles and squares) and medium diculty (coins) the GA method achieved 100% recall and 100% precision. Results of both methods on the dicult problem were disappointing, but the GA method was clearly superior. However the performance is consistent with the performance of other methods on similar dicult problems. The GA based method has a number of disadvantages: An empirical search is required for good values of parameters, such as population size, crossover rate, mutation rate and limits for delta1 and delta2. However this is oset by

6 Class "haem" Recall (%) > Precision (%) Basic Approach With GA NA NA NA NA NA NA Class "micro" Recall (%) Precision (%) Basic Approach With GA Table 3: Comparison of object detection in the retina pictures using the basic approach and the GA approach the increase in detection accuracy. The training times are quite long. Some of the runs took longer than 24 hours on a SPARC station. We are investigating ways of shortening the training times. Overall, the GA method produces pixel based networks that work well on objects on a relatively uniform background. However more work is needed for objects on non uniform and cluttered backgrounds such as our retina pictures. References [1] V. Ciesielski and J. Zhu. A very reliable method for detecting bacterial growths using neural networks. In Proceedings of the International Joint Conference on Neural Networks, pages 62{67, Beijing, November [2] Victor Ciesielski and Je Riley. An evolutionary approach to training feed forward and recurrent neural networks. In L. C. Jain and R. K. Jain, editors, Proceedings of the Second International Conference on Knowledge Based Intelligent Electronic Systems (KES'98, pages 596{602, Adelaide, April [3] Jack Jean and Jin Wang. Weight smoothing to improve network generalization. IEEE Transactions on neural networks, 5(5):752{763, September [4] Rajendra Krishnan and Victor Ciesielski. 2DELTA-GANN: a new approach to using genetic algorithms to train neural networks. In A. C. Tsoi, editor, Proceedings of the Fifth Australian Neural Networks Conference, pages 38{41, University of Queensland, Brisbane, Feb [5] M. Shirvaikar and M. Trivedi. A network lter to detect small targets in high clutter backgrounds. IEEE Transactions on Neural Networks, 6(1):252{257, Jan [6] Mengjie Zhang and Victor Ciesielski. Centred weight initialization to improve the performance of network training speed and the performance of object detection. Technical Report TR 98-10, Department of Computer Science, RMIT University, Melbourne, Australia, May 1998.

School of Mathematics, Statistics and Computer Science. Computer Science. Object Detection using Neural Networks and Genetic Programming

T E W H A R E W Ā N A N G A O T E Ū P O K O O T E I K A A M Ā U I ÎÍÏ V I C T O R I A UNIVERSITY OF WELLINGTON School of Mathematics, Statistics and Computer Science Computer Science Object Detection using