In: Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 30, Nr. 1 (2008), p Group of Adjacent Contour Segments for Object Detection 學生 : 戴玉書 教授 : 王聖智 老師 LEAR laboratory - INRIA Grenoble

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

Introduction Test image Training images

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

Contour segment network

 1. Edgels extracted with Berkeley boundary detector  2. Edgel-chains partitioned into straight contour segments  3. segments connected at edgel-chains’ endpoints and junctions Contour segment network [Ferrari et al. ECCV 2006]

- Links between edgel-chains

- Connects between segments

Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues [David R. Martin, Member, IEEE, Charless C. Fowlkes, and Jitendra Malik, Member, IEEE]

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

k adjacent segments (kAS) Three kinds of 2AS

- Descriptor of kAS 

 D(a, b) - Comparing kAS

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

 kAS can be detected by a depth-first search started from every segment Detecting kAS

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

 Using a clique-partitioning (CP) approach  Let G be a complete graph whose nodes are the training kAS, and arcs are weighted by d − D(a, b) kAS codebook Each resulting clique is a cluster of similar kAS D(1, 2) D(1, 3) D(2, 3)

- Clique-partitioning (CP) approach 

The 35 most frequent 2AS types from the codebook The 35 most frequent 3AS from the codebook

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

|B| ・ |C| - dimensional window descriptor Object class detection -Training 1. Subdivide window into tiles 2. Compute a separate bag of PAS per tile 3. Concatenate these semi-local bags

- Training 1. Learn mean positive window dimensions 2. Determine number of tiles T 3. Collect positive example descriptors 4. Collect negative example descriptors: slide window over negative training images 5. Train a linear SVM

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

Object class detection -Testing 1. Slide window of aspect ratio, at multiple scales 2. SVM classify each window

 Introduction  Contour segment network  k adjacent segments (kAS) -Detecting kAS -kAS CODEBOOK  Object class detection -Training -Testing  Result Outline

Result

Red : 2AS Blue : HoG