Object Segmentation Presented by Sherin Aly 1 What

Object Segmentation Presented by Sherin Aly 1

What is a ‘Good Segmentation’?

http: //www. eecs. berkeley. edu/Research/Projects/CS/vision/groupin g/resources. html

Learning a classification model for segmentation Xiaofeng Ren and Jitendra Malik 4

methodology • Two-classification model • Over segmentation as preprocessing • They use classical Gestalt cues – Contour, texture, brightness and continuation • A linear classifier is used for training 5

Good Vs Bad segmentation a) Image from Corel Imagebase b) superimposed with a human marked segmentation c) Same image with Bad segmentation 6

How do we distinguish good segmentations from bad segmentations? 7

How? • Use “Classical Gestalt cues” – proximity, similarity and good continuation • Instead of Ad-hoc features combination decision about 8

Gestalt Principles of Grouping In order to interpret what we receive through our senses, we attempt to organize this information into certain groups. http: //allpsych. com/psychology 101/perception. html 9

Methodology • • • Preprocessing Feature extraction Feature evaluation Training Optimization Find good segmentaion 10

Preprocessing • Local • Coherent • Preserve structure • Contour • texture Superpixel map K=200 Reconstruction of human segmentation from Superpixels a contour-based measure is used to quantify this approximation 11

Tolerance 1, 2, and 3 The percentage of human marked boundaries covered by the superpixel maps 12

Feature Extraction 1. inter-region texture similarity 2. intra-region texture similarity 3. inter-region brightness similarity 4. intra-region brightness similarity 5. inter-region contour energy 6. intra-region contour energy 7. curvilinear continuity 13

Feature Extraction 1. inter-region texture similarity 2. intra-region texture similarity 3. inter-region brightness similarity 4. intra-region brightness similarity 5. inter-region contour energy 6. intra-region contour energy 7. curvilinear continuity 14

Feature Extraction 1. inter-region texture similarity 2. intra-region texture similarity 3. inter-region brightness similarity 4. intra-region brightness similarity 5. inter-region contour energy 6. intra-region contour energy 7. curvilinear continuity 15

Power of Gestalt cues = 16

Training the classifier • simple logistic regression classifier, Empirical distribution of pairs of features 17

Precision is the fraction of detections which are true positives. Recall is the fraction of true positives which are detected 18

Conclusion • There simple linear classifier had promising results on a variety of natural images. • boundary contour is the most informative grouping cue, and it is in essence discriminative. 19

Pros & Cons • Cons – The larger spatial support that superpixels provide, allowing more global features to be computed than on pixels alone. – The use of superpixels improves the computational efficiency – Super. Pixels technique is very applicable • Pros – Might fall in Local Minima 20

Combining Top-down and Bottom-up Segmentation Eran Borenstein Eitan Sharon Shimon Ullman 21

Motivation • Bottom-Up segmentation – Rely on continuity principle – Capture image properties “texture, grey level uniformity and contour continuity” – Segmentation based on similarities between image regions • How can we capture prior knowledge of a specific object (class)? – Answer: Top-Down Segmentation – use prior knowledge about an object Credit: Joseph Djugash

Bottom-Up Segmentation Credit: Joseph Djugash Slides from Eitan Sharon, “Segmentation and Boundary Detection Using Multiscale Intensity Measurements ”.

Normalized-Cut Measure Credit: Joseph Djugash Slides from Eitan Sharon, “Segmentation and Boundary Detection Using Multiscale Intensity Measurements ”.

Top-Down approach Input Credit: Joseph Djugash Fragments Matching Cover

Another step towards the middle Top-Down Bottom-Up Credit: Joseph Djugash

Some Definitions & Constraints • Measure of saliency h(Γi), hi є [0, 1) • A configuration vector s contains labels si (1/1) of all the segments (Si) in the tree • The label si can be different from its parent’s label s i – • Cost function for a given s Defines the weighted edge between Si & Si – Top-down term Bottom-up term

Classification Costs • The terminal segments of the tree determine the final classification • The top-down term is defined as: • The saliency of a segment should restrict its label (based on its parent’s label) • The bottom-up term is defined as:

Confidence Map • Evaluating the confidence of a region: • Causes of Uncertainty of Classification – Bottom-up uncertainty – regions where there is no salient bottom-up segment matching the top-down classification – Top-down uncertainty – regions where the topdown classification is ambiguous (highly variable shape regions) • The type of uncertainty and the confidence values can be used to select appropriate additional processing to improve segmentation

Results • Calculate average distance between a given segmentation contour and a benchmark contour. • Removing from the average all contour points having a confidence measure less than 0. 1. • The resulting confidence map efficiently separated regions of high and low consistency. • The combined scheme improved the top-down contour by over 67% on average. • This improvement was even larger in object parts with highly variable shape. 31

The initial classification map T(x, y) Results (cont. ) Buttom up • top-down process may produce a figure-ground approximation that does not follow the image discontinuities. • Salient bottom-up segments can correct these errors and delineate precise region boundaries

Results III (cont. )

Results III (cont. ) the top-down completely misses a part of the object. The confidence map may be helpful in identifying such cases,

Results III (cont. ) bottom-up segmentation may be insufficient in detecting the figure-ground contour, and the top-down process completes the missing information

Results III (cont. )

Results III (cont. ) Salient bottom-up segments can correct these errors and delineate precise region boundaries

Conclusion • Buttom-up and top-down merits • Provide reliable confidence map • It take into account all discontinuities at all scales But: • If the object is assigned a given category, the specific features cannot be adopted for other categories 38

Constrained Parametric Min-Cuts for Automatic Object Segmentation Joao Carreira Cristian Sminchisescu 39

Traditional Segmentation: Finding Homogeneous Regions g. Pb-owt-ucm: P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. PAMI 2010. 40

Bottom-up Object Segmentation Conventional Bottom-up Segmentation Proposed approach 1. Split multiple times 2. Retain object-like segmentations High redundancy Credit: J. Carreira

Bottom-up Object Segmentation A single multi-region segmentation or a hierarchy 42 Credit: J. Carreira

Proposed Bottom-up Object Segmentation Credit: J. Carreira robust set of overlapping figure-ground segmentations single-shot multiregion segmentation superpixels 43 Segments with object-like regularities

Constrained Parametric Min-Cuts for Automatic Object Segmentation Figure ground segmentation by growing regions around seeds parametric max-flow solver Ranking 44 Credit: J. Carreira

Constrained Parametric Min-Cuts for Automatic Object Segmentation 45 Credit: J. Carreira

Initialization • Foreground – Regular 5 x 5 grid geometry – Centroids of large N-Cuts regions – Centroids of superpixels closest to grid positions • Background – – Full image boundary Horizontal boundaries Vertical boundaries All boundaries excluding the bottom one Performance broadly invariant to different initializations

Generating a segment pool: constrained min-cut object min cut hard constraint background 47 Credit: J. Carreira

Generating a Segment Pool: Constrained Parametric Min-Cuts 48 Credit: J. Carreira

Generating a Segment Pool: Constrained Parametric Min-Cuts 49 Credit: J. Carreira

Generating a Segment Pool: Constrained Parametric Min-Cuts 50 Credit: J. Carreira

Generating a Segment Pool: Constrained Parametric Min-Cuts Can solve for all values of object bias in the same time complexity of solving a single min-cut using a parametric max-flow solver 51 Credit: J. Carreira

Fast Rejection Large set of initial segmentations (~5500) High Energy Low Energy ~2000 segments with the lowest energy Credit: Sasi. Kanth Bendapudi Yogeshwar Nagaraj Cluster segments based on spatial overlap (at least 0. 95) Lowest energy member of each cluster (~154 in PASCAL VOC)

Constrained Parametric Min-Cuts for Automatic Object Segmentation Credit: J. Carreira • ranks all the sampled object segmentations • discard all but a small subset of confident 53 ones.

Ranking object hypotheses mid-level, category independent features Boundary – normalized boundary energy Region – location, perimeter, area, Euler number, orientation, contrast with background Gestalt – convexity, smoothness Good Bad High boundary energy Low boundary energy Smooth. Non smooth. Euler number = 0 High Euler number 54 Credit: J. Carreira

Segment Ranking • Model data using a host of features – Graph partition properties – Region properties – Gestalt properties • Apply Features Normalization • Train regressor with the largest overlap ground-truth segment using Random Forests • Diversify similar rankings using Maximal Marginal Relevance (MMR)

Graph Partition Properties • Cut – Sum of affinities along segment boundary • Ratio Cut – Sum along boundary divided by the number • Normalized Cut – Sum of cut and affinity in foreground and background • Unbalanced N-cut – N-cut divided by foreground affinity • Thresholded boundary fraction of a cut

Region Properties • • Area Perimeter Relative Centroid Bounding Box properties • Fitting Ellipse properties • Eccentricity • Orientation • Convex Area • Euler Number • Diameter of Circle with the same area of the segment • Percentage of bounding box covered • Absolute distance to the center of the image

Gestalt Properties • • Inter-region texton similarity Intra-region texton similarity Inter-region brightness similarity Intra-region brightness similarity Inter-region contour energy Intra-region contour energy Curvilinear continuity Convexity – Ratio of foreground area to convex hull area

Feature Importance for the Random Forest regressor

Feature Importance

How to Model Segment Quality ? Best overlap with a ground truth object computed by intersection-over-union. 64 Credit: J. Carreira

Diversifying the Ranking Segment Ranking using Maximum Marginal Relevance Best two hypotheses Middle two hypotheses Worst two hypotheses Original Diversified 66

Performance Credit: Sasi. Kanth Bendapudi Yogeshwar Nagaraj

Ranking 79 Credit: J. Carreira

Running Demos • Methodologies employed – Kmeans using: • • • Texture RGB Texture + RGB + HSV Texture + Lab + HSV 80

Running Demos • Data set used – Microsoft Research Cambridge Object Recognition Image Database, version 1. 0. – Used: 7 classes with 23 per class • • Animal-grass Trees-sky-grass Buildings-sky-grass Airplanes-sky-grass Animal-grass Faces-BG Car-wall-ground 81

Experiment Results Features Texture + RGB RGB +HSV Texture+Lab+ HSV Animal-grass 72. 7% 74. 1% 72. 3% 72. 6% 74. 1% Trees-sky-grass 37. 1% 40. 7% 38. 2% 37. 1% Buildings-skygrass 44. 6% 42. 8% 51. 9% 45. 4% 44. 7% Airplanes-skygrass 58. 8% 54. 6% 59. 7% 58. 7% Animal-grass 64. 8% 69. 3% 71% 64. 9% Faces-BG 100% 100% Car-wall-ground 67. 2% 68. 4% 64. 9% 67. 2% Mean 63. 6% 63. 5% 65. 3% 64. 6% 63. 8% 82

Microsoft Research Cambridge Object Recognition Image Database, v Experiment Results Features One iteration Elapsed time is Texture + RGB 7. 42 secs Overall Elabsed 19. 9 time for mins experiment RGB +HSV Texture+Lab+ HSV 12. 26 secs. 1. 62 secs 1. 5 secs 7. 84 sec 32. 9 mins 4. 4 mins 4 min 21 mins 83

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Acknowledgment • Dr. Devi Parikh • Dr. Joao Carreira 88