图像处理_Caltech 101(加利福尼亚理工学院101类图像数据库)

Caltech 101(加利福尼亚理工学院101类图像数据库)

数据摘要：

Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels.

We have carefully clicked outlines of each object in these pictures, these are included under the 'Annotations.tar'. There is also a matlab script to view the annotaitons, 'show_annotations.m'.

The Caltech 101 dataset consists of a total of 9146 images, split between 101 different object categories, as well as an additional

background/clutter category.

Each object category contains between 40 and 800 images on average. Common and popular categories such as faces tend to have a larger number of images than less used categories. Each image is about 300x200 pixels in dimension. Images of oriented objects such as airplanes and motorcycles were mirrored to be left-right aligned, and vertically oriented structures such as buildings were rotated to be off axis.

中文关键词：

识别,多类,分类,标注,轮廓,

英文关键词：

Recognition,Multi class,Categories,Annotations,Outline,

数据格式：

IMAGE

数据用途：

To train and test several Computer Vision recognition and classification algorithms.

数据详细介绍：

Caltech 101

Description

Pictures of objects belonging to 101 categories. About 40 to 800 images per category. Most categories have about 50 images. Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc 'Aurelio Ranzato. The size of each image is roughly 300 x 200 pixels.

We have carefully clicked outlines of each object in these pictures, these are included under the 'Annotations.tar'. There is also a matlab script to view the annotaitons, 'show_annotations.m'.

How to use the dataset

If you are using the Caltech 101 dataset for testing your recognition algorithm you should try and make your results comparable to the results of others. We suggest training and testing on fixed number of pictures and repeating the

experiment with different random selections of pictures in order to obtain error bars. Popular number of training images: 1, 3, 5, 10, 15, 20, 30. Popular numbers of testing images: 20, 30. See also the discussion below.

When you report your results please keep track of which images you used and which were misclassified. We will soon publish a more detailed experimental protocol that allows you to report those details. See the Discussion section for more details.

Literature

Papers reporting experiments on Caltech 101 images:

1. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. L. Fei-Fei, R. Fergus, and P. Perona. CVPR 2004, Workshop on Generative-Model Based Vision. 2004

2. Shape Matching and Object Recognition using Low Distortion Correspondence. Alexander C. Berg, Tamara L. Berg, Jitendra Malik. CVPR 2005

3. The Pyramid Match Kernel:Discriminative Classification with Sets of Image Features. K. Grauman and T. Darrell. International Conference on Computer Vision (ICCV), 2005.

4. Combining Generative Models and Fisher Kernels for Object Class Recognition Holub, AD. Welling, M. Perona, P. International Conference on Computer Vision (ICCV), 200

5.

5. Object Recognition with Features Inspired by Visual Cortex. T. Serre, L. Wolf and T. Poggio. Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), IEEE Computer Society Press, San Diego, June 2005.

6. SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition. Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik. CVPR, 2006.

7. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. CVPR, 2006 (accepted).

8. Empirical study of multi-scale filter banks for object categorization, M.J. Marín-Jiménez, and N. Pérez de la Blanca. December 2005. Tech Report.