Bag-of-Words Model, Implicit Shape Model, Deformable Part-based Model
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00:00:00
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Lecture 16: Part-based Models for Object Categorization |
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00:00:08
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Announcements |
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00:02:14
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Course Outline |
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00:04:33
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Topics of This Lecture |
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00:04:39
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Recap: Recognition with Local Features |
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00:06:07
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Recap: Indexing features |
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00:10:20
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Extension: tf-idf Weighting |
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00:13:40
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Recap: Fast Indexing with Vocabulary Trees |
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00:15:56
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Application for Content Based Img Retrieval |
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00:17:23
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Video Google System |
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00:19:36
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Collecting Words Within a Query Region |
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00:20:06
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Example Results |
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00:20:16
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More Results |
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00:21:09
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Recap: Geometric Verification by Alignment |
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00:22:45
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Applications: Aachen Tourist Guide |
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00:24:31
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Applications: Fast Image Registration |
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00:25:16
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Applications: Mobile Augmented Reality |
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00:26:38
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Topics of This Lecture |
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00:28:46
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Recognition of Object Categories |
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00:31:42
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Part-Based Models |
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00:33:42
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Different Connectivity Structures |
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00:37:12
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Topics of This Lecture |
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00:37:21
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Analogy to Documents |
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00:38:38
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Object -> Bag of 'words' |
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00:40:15
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Bag of Visual Words |
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00:40:49
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Similarity, Bags-of-Textons for Texture Repr. |
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00:46:11
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Comparing Bags of Words |
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00:46:58
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Learning/Recognition with BoW Histograms |
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00:47:48
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Recap: Categorization with Bags-of-Words |
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00:49:05
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BoW for Object Categorization |
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00:55:44
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Limitations of BoW Representations |
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00:56:33
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Spatial Pyramid Representation |
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00:57:46
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Summary: Bag-of-Words |
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00:58:49
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Topics of This Lecture |
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00:59:21
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Implicit Shape Model (ISM) |
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01:00:56
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Implicit Shape Model: Basic Idea |
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01:02:15
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Implicit Shape Model - Representation |
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01:03:53
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Implicit Shape Model - Recognition |
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01:05:41
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Example: Results on Cows |
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01:09:06
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Scale Invariant Voting |
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01:09:57
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Detection Results |
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01:10:33
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Detections Using Ground Plane Constraints |
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01:11:57
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Extension: Rotation-Invariant Detection |
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01:12:42
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Sometimes, Rotation Invariance is Needed... |
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01:13:07
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Implicit Shape Model - Segmentation |
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01:13:51
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Example Results: Motorbikes |
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01:14:22
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You Can Try It At Home... |
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01:15:17
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Topics of This Lecture |
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01:16:25
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Starting Point: HOG Sliding-Window Detector |
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01:17:40
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Deformable Part-based Models |
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01:18:32
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2-Component Bicycle Model |
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01:20:20
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Object Hypothesis |
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01:21:19
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Score of a Hypothesis |
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01:22:26
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Recognition Model |
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01:24:22
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Results: Persons |
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01:24:43
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Results: Bicycles |
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01:25:11
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False Positives |
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01:25:27
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Results: Cats |
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01:25:36
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you Can Try It At Home... |
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01:26:27
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References and Further Reading |