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NORB: Generic Object Recognition in Images

  • Time Period: September 2003 - present.
  • Participants: Fu Jie Huang, Yann LeCun (Courant Institute/CBLL), Leon Bottou (NEC Labs).
  • Talks:
    • Slides: End-to-End Learning of Object Categorization with Invariance to Pose, Illumination, and Clutter. Slides of a talk delivered at CVPR Workshop on Object Recognition, Washington DC, June 2004. [DjVu (2.1MB)].
  • Publications:
    • [LeCun, Huang, Bottou, 2004]. Learning Methods for Generic Object Recognition with Invariance to Pose and Lighting Proceedings of CVPR 2004.
  • DatasetDownload the NORB dataset.
  • Support: this project is supported by National Science Foundation under grants numbers 0535166, and 0325463.

The recognition of generic object categories with invariance to pose, lighting, diverse backgrounds, and the presence of clutter is one of the major challenges of Computer Vision.

We are developing learning systems that can recognize generic object purely from their shape, independently of pose, illumination, and surrounding clutter.

The NORB dataset (NYU Object Recognition Benchmark) contains stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions (for a total of 194,400 individual images).

The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other 5 for testing.

The picture shows the 25 objects used for training (left panel) and the 25 different objects used for testing (right panel). There are five object categories: animals, human figures, airplanes, trucks and cars.

Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used to train and test nearest neighbor methods, Support Vector Machines, and Convolutional Networks, operating on raw pixels or on PCA-derived features.

The NORB Dataset

Experiments were conducted with four datasets generated from the normalized object images. The first two datasets were for pure categorization experiments (a somewhat unrealistic task), while the last two were for simultaneous detection/segmentation/recognition experiments.

All datasets used 5 instances of each category for training and the 5 remaining instances for testing. In the normalized dataset, 972 images of each instance were used: 9elevations, 18 azimuths (0 to 340 degrees every 20 degrees), and 6 illuminations, for a total of 24,300 training samples and 24,300 test samples. In the various jittered datasets, each of the 972 images of each instance were used to generate additional examples by randomly perturbing the position ([-3, +3] pixels), scale (ratio in [0.8, 1.1]), image-plane angle ([-5, 5] degrees), brightness ([-20, 20] shifts of gray levels), contrast ([0.8, 1.3] gain) of the objects during the compositing process. Ten drawings of these random parameters were drawn to generate training sets, and one or two drawings to generate test sets.

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Image capturing setup.

In the textured and cluttered datasets, the objects were placed on randomly picked background images. In those experiments, a 6-th category was added: background images with no objects (results are reported for this 6-way classification). In the textured set, the backgrounds were placed at a fixed disparity, akin to a back wall orthogonal to the camera axis at a fixed distance. In the cluttered datasets, the disparities were adjusted and randomly picked so that the objects appeared placed on highly textured horizontal surfaces at small random distance from that surface. In addition, a randomly picked ``distractor'' object from the training set was placed at the periphery of the image.

Examples of the various lighting conditions for two elevations)
  • normalized-uniform set: 5 classes, centered, unperturbed objects on uniform backgrounds. 24,300 training samples, 24,300 testing samples.
  • jittered-uniform set: 5 classes, random perturbations, uniform backgrounds. 243,000 training samples (10 drawings) and 24,300 test samples (1 drawing)
  • jittered-textured set: 6 classes (including one background class) random perturbation, natural background textures at fixed disparity. 291,600 training samples (10 drawings), 58,320 testing samples (2 drawings).
  • jittered-cluttered set: 6 classes (including one background class), random perturbation, highly cluttered background images at random disparities, and randomly placed distractor objects around the periphery. 291,600 training samples (10 drawings), 58,320 testing samples (2 drawings).
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Compositing process. top left: raw image; top right: chroma-keyed object mask; bottom left: cast shadow coefficient mask; bottom right: composite image with cast shadow.

Occlusions of the central object by the distractor occur occasionally in the jittered cluttered set. Most experiments were performed in binocular mode (using left and right images), but some were performed in monocular mode. In monocular experiments, the training set and test set were composed of all left and right images used in the corresponding binocular experiment. Therefore, while the number of training samples was twice higher, the total amount of training data was identical. Examples from the jittered-textured and jittered-cluttered training set are shown below

Examples from the jittered-textured

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examples from the jittered-cluttered dataset. This dataset is availabe for download.


On the Normalized-Uniform Dataset

ClassifierError Rate
Linear Classifier, binocular 30.2% error
K-Nearest Neighbors on raw stereo images 18.4% error
K-Nearest Neighbors on 95 PCA features 16.6 error
Pairwise Support Vector Machine on raw stereo images NO CONVERGENCE
Pairwise SVM on 48x48 monocular images 13.9% error
Pairwise SVM on 32x32 monocular images 12.6% error
Pairwise SVM on 95 PCA features 13.3 error
Convolutional Network "LeNet7" 6.6% error
Convolutional Network "LeNet7" with pose manifold 6.2% error

The first 60 principal components extracted from the normalized-uniform training set. Unlike with eigen-faces these "eigen-toys" are not recognizable and have symmetries because the objects are seen from every angle in the training set.

On the Jittered-Cluttered Dataset

ClassifierError Rate
Convolutional Network "LeNet7", binocular 7.8% error
Convolutional Network "LeNet7", monocular 20.8% error

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Architecture of the convolutional net "LeNet 7". This network has 90,857 trainable parameters and 4.66 Million connections. Each output unit is influenced by a receptive field of 96x96 pixels on the input.

Learned kernels from the first layer of the binocular convolutional network.

Learned kernels from the third layer of the binocular convolutional network.

Results and Examples

The convolutional network can be very efficiently applied to all locations on a large input image. For example, applying LeNet 7 to a single 96x96 window requires 4.66 Million multiply-accumulate operations. But applying LeNet 7 to every 96x96 windows, shifted every 12 pixels, over a 240x240 image (169 windows) requires only 47.5 Million multiply-accumulate operations. Applying a non-convolutional classifier with the same complexity to every such 96x96 window would consume 788 Million operations (4.66 million times 169).

The network can be applied to images at multiple scales to ensure scale invariance.

A system was built around LeNet 7, that can detect and recognize objects in natural images. The system runs in real time (a few frames per second) on a laptop connected to a USB camera. Examples of outputs from that system are shown below.

Scenes with objects from the NORB dataset

Various scenes with other objects

Natural Scenes

NOTE: The system was not trained on natural images.

A few mistakes

Examples with the Internal State of the Convolutional Network