LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH
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1 Università degli Studi dell'insubria Varese, Italy LEARNING OBJECT SEGMENTATION USING A MULTI NETWORK SEGMENT CLASSIFICATION APPROACH Simone Albertini Ignazio Gallo, Angelo Nodari, Marco Vanetti albertini.simone@gmail.com February 25th, 2012
2 The problem Object of interest segmentation Prior PriorKnowledge Knowledge Object Object Segmentation Segmentation The class the object belongs to Useful for various high level tasks 2
3 The context Commercial products images: Low resolution Artifacts 3
4 The proposed solution Multi-net for Object Segmentation (MNOS) Inner node C: MLP network with its configuration P Leaf F: feature node 4
5 The proposed solution Each inner node C takes as input several map-images and predicts an output map Node C Output Map Input Maps (one for each child node) The Network predicts the probability each pixel belongs to the Object of Interest 5
6 The proposed solution A leaf node only applies operators and transformations to the original image: Extracts color channels (RGB or HSV) 6
7 The proposed solution A leaf node only applies operators and transformations to the original image: Haar, Hog, High Frequencies 7
8 Multi-Net for Object Detection Feed-forward and Multi-Neural Networks model 8
9 Two possible internal nodes Sliding window approach: Pattern generated from the raw intensity values of the input images Gallo, I. and Nodari, A. (2011). Learning object detection using multiple neural netwoks. In VISAPP INSTICC Press. Segment-based approach: Input images are partitioned and features are extracted from each segment 9
10 Two possible internal nodes Nodes with sliding windows operate at pixel level 10
11 Two possible internal nodes Nodes with sliding windows operate at pixel level 11
12 Two possible internal nodes Nodes with sliding windows operate at pixel level Good for detection Objective: sharp and neat edges to segment the object of interest 12
13 Two possible internal nodes Nodes with sliding windows operate at pixel level 13
14 Segments Internal node Features Features Original Image Input maps Geometry based Histogram based Moments For each segment Build Build Output Output map map Output Output predictions predictions MLP MLP Output map 14
15 Segment Features Geometric: Area, Perimeter Perimeter over area Bounding box locations and dimensions Intensity histogram Hu Moments 15
16 The MNOS tree We should feed the majority of the node with at least some leaf High frequency filters seems to be very effective Usage of sliding window nodes in the low levels of the tree 16
17 Experiments Custom Dataset: Drezzy 8 classes: Jpeg images Resolution: 100 x 100 or 200 x 200 pixels Bags (285) Shoes (400) Hats (158) Ties (203) Man Clothing (150) Man Underwear (278) Woman Clothing (355) Woman Underwear (239) VOC Challenge metrics adopted 17
18 First experiment Only nodes based on segments classification Dataset Obj Acc Train Obj Acc Test Bags 83,92 79,13 Shoes 80,05 77,76 Hats 68,63 64,25 Ties 90,42 77,76 Man Clothing 73,09 68,06 Man Underwear 40,97 38,75 Woman Clothing 52,53 57,84 Woman Underwear 36,33 35,13 18
19 First experiment The network cannot solve the problem: with complex backgrounds when we have other objects with high contrast 19
20 Hybrid Model Hybrid model: Sliding window and segment classification Output Map Features Segments Sliding Window 20
21 Hybrid Model Hybrid model: Sliding window and segment classification Dataset Acc test Diff Bags 79,00-0,13 Shoes 88, ,63 Hats 62,55-1,70 Ties 81,52 + 3,76 Man Clothing 73,40 + 5,34 70 Man Underwear 65, ,50 60 Woman Clothing 62,64 + 4,80 50 Woman Underwear 66, ,55 40 Output Map Features Seg SW Bags Shoes Hats Ties Man C. Only segments Man U. Woman C. Woman U. Hybrid 21
22 Hybrid Model Hybrid model: Sliding window and segment classification 22
23 Post Processing with GrabCut Two approaches: Bounding Box vs Region Mask The MNOS segmentation mask is labeled in order to initialize the GrabCut 23
24 Post Processing with GrabCut Dataset GrabCut Diff Bags 89,29 +10,29 Shoes 92,70 +4,31 Hats 82,19 +19,64 Ties 92,39 +10,87 Man Clothing 81,50 +8,10 Man Underwear 80,10 +14,85 Woman Clothing 66,97 +4,33 80 Woman Underwear 61,22-5, Bags Shoes Hats Ties MNOS Man C Man U Woman C Woman U GrabCut 24
25 Post Processing with GrabCut 25
26 Results with VOC 2011 dataset Class MNOS GC Voc Best Class MNOS GC Voc Best Aeroplane 36,04 55,60 54,3 DiningTable 26,01 26,42 30,1 Bicycle 14,99 13,43 23,9 Dog 34,56 38,16 33,9 Bird 24,18 37,06 46,0 Horse 31,11 42,15 49,1 Boat 31,05 36,11 35,3 Motorbike 51,13 49,44 54,4 Bottle 20,80 19,11 49,4 Person 32,73 35,82 46,4 Bus 50,93 57,14 66,2 Pottedplant 19,38 24,44 28,8 Car 37,27 37,52 56,2 Sheep 31,26 31,58 51,3 Cat 36,82 39,33 46,1 Sofa 26,45 28,02 26,4 Chair 4,78 9,13 15,0 Train 50,26 52,37 44,9 Cow 40,65 54,07 47,4 Tvmonitor 18,18 26,54 45,8 Mean MNOS: 30,93 Mean GC: 35,67 Best Voc Mean: 43,3 26
27 Results with VOC 2011 dataset Class MNOS GC Aeroplane 36,04 55,60 Voc Best 54,3 Class MNOS GC Voc Best 23,9 DiningTable Dog 26,01 34,56 26,42 38,16 30,1 33,9 Bicycle 14,99 13,43 Bird 24,18 37,06 46,0 Horse 31,11 42,15 49,1 Boat 31,05 36,11 35,3 Motorbike 51,13 49,44 54,4 Bottle 20,80 19,11 49,4 Person 32,73 35,82 46,4 Bus 50,93 57,14 66,2 Pottedplant 19,38 24,44 28,8 Car 37,27 37,52 56,2 Cat 36,82 39,33 46,1 Sheep Sofa 31,26 26,45 31,58 28,02 51,3 26,4 Chair Cow 4,78 40,65 9,13 54,07 15,0 47,4 Train 50,26 52,37 44,9 Tvmonitor 18,18 26,54 45,8 Mean MNOS: 30,93 Mean GC: 35,67 Best Voc Mean: 43,3 27
28 Commercial application Segmentator Segmentator Offers export Segmentation masks Images of the products Visual VisualInformation Information Extraction Extraction Color Color Texture Texture 28
29 Commercial application 29
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