cvpr2009: class specific hough forest for object detection
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This document presents class-specific Hough forests, a method for object detection that combines spatial information from object parts with class information during learning. The method trains random forests to learn the relationship between image patches and their spatial position relative to the object center. At detection time, the forests cast class-specific votes in 3D or 4D space (x,y,scale (or x,y,scale,ratio)) that are accumulated to detect objects. The method achieves state-of-the-art results on several datasets and offers advantages over related Hough-based methods such as combining spatial and class information during learning.
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We propose a novel approach for instance-level image retrieval. It produces a global and compact fixed-length representation for each image by aggregating many region-wise descriptors. In contrast to previous works employing pre-trained deep networks as a black box to produce features, our method leverages a deep architecture trained for the specific task of image retrieval. Our contribution is twofold: (i) we introduce a ranking framework to learn convolution and projection weights that are used to build the region features; and (ii) we employ a region proposal network to learn which regions should be pooled to form the final global descriptor. We show that using clean training data is key to the success of our approach. To that aim, we leverage a large scale but noisy landmark dataset and develop an automatic cleaning approach. The proposed architecture produces a global image representation in a single forward pass. Our approach significantly outperforms previous approaches based on global descriptors on standard datasets. It even surpasses most prior works based on costly local descriptor indexing and spatial verification. We intend to release our pre-trained model.What's hot (20)
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cvpr2009: class specific hough forest for object detection
- 1. Class-Specific Hough Forests for Object Detection Juergen Gall1 and Victor Lempitsky2 1BIWI,ETH Zurich 1Max-Planck-Institute for Informatics 2Microsoft Research Cambridge
- 2. Motivation Parts of an object provide useful spatial information Classification of object parts (foreground/background) Combine spatial information and class information during learning
- 3. Overview
- 4. Related Work Explicit model of object: Detect parts → Assemble parts together (e.g. Pictorial Structures) Implicit model of object: Learn relation of parts Codebook based on appearance (e.g. Leibe et al. IJCV’08) Codebook based on appearance and spatial information (Opelt et al. IJCV’08; Shotton et al. PAMI’08) Grid-based classifier for object parts (Winn and Shotton CVPR’06) Class-specific Hough forest: Generalized Hough transform within Random forest framework (Breiman ML’01)
- 5. Random Forest Image patch: Binary tests: Binary tests are selected during training from a random subset of all binary tests
- 6. Training Training set: Class information: ci (class label) Spatial information: di (relative position to object center)
- 7. Binary Tests Selection Test with optimal split: Class-label uncertainty: Offset uncertainty: Interleaved: Type of uncertainty is randomly selected for each node
- 8. Leaves {Pi ∈ L : ci = 1}{Pi ∈ A : ci = 0} Class probability: C L = {Pi ∈ L : ci = 0}{Pi ∈ A : ci = 1} + {Pi ∈ L : ci = 1}{Pi ∈ A : ci = 0}
- 9. Spatial probability For location x and given image patch I(y) and tree T Over all trees: Accumulation over all image patches:
- 10. Detection
- 11. Multi-Scale and Multi-Ratio Multi Scale: 3D Votes (x, y, scale) Multi-Ratio: 4D Votes (x, y, scale, ratio)
- 12. UIUC Cars - Multi Scale Wrong (EER) Correct
- 13. Comparison
- 18. Recenter Object’s center ≠ Centre of bounding box Split training data → Estimate centers iteratively
- 19. Weizmann Horses
- 20. Summary Superior to previous methods using related techniques State-of-the-art for several datasets Advantages over related Hough-based methods: Combine spatial information and class information No sparse features like SIFT GPU → real-time performance is feasible Large and high-dimensional datasets Bounding box-annotated training data is sufficient Focus: Get strong signal → Improve Detection 2-class problem → Multi-class problem
- 21. Thank you for your attention. The major part of the research project was undertaken when Juergen Gall was an intern with Microsoft Research Cambridge. The advice from Toby Sharp, Jamie Shotton, and other members of the Computer Vision Group at MSRC is gratefully acknowledged. We would also like to thank all the researchers, who have collected and published the datasets we used for the evaluation.