1. Efficient Image Classification on Vertically Decomposed Data
Taufik Abidin, Aijuan Dong, Hongli Li, and William Perrizo
Computer Science North Dakota State University
The 1st IEEE International Workshop on Multimedia Databases and Data Management (MDDM-06)

2. Outline Image classification
The application of SMART-TV algorithm in image classification
SMART-TV algorithm
Experimental results
Summary

3. Image Classification Why classifying images?
The proliferation of digital images
The need to organize them into semantic categories
for effective browsing
for effective retrieval
Techniques for image classification:
SVM, Bayesian, Neural Network, KNN

4. Image Classification Cont.
In this work, we focus on KNN method
KNN is widely used in image classification:
Simple and easy to implement
Good classification results
Problems:
Classification time is linear to the size of image repositories
When the repositories are very large, contains millions of images, KNN is impractical

5. Our Contributions
We apply our recently developed classification algorithms, a.k.a. SMART-TV for image classification task and analyze its performance
We demonstrate that SMART-TV, a classification algorithm that uses P-tree vertical data structure, is fast and scalable to very large image databases
We show that for Corel images a combination of color and texture features are a good alternative to represent the low-level of images

6. Image Preprocessing
We extracted color and texture features from the original pixel of the images
We created 54-dimension color histogram in HVS (6x3x3) color space for color features and created 8 multi-resolutions Gabor filter (4 orientations and 2 scales) to extract texture features of the images (see B.S. Manjunath, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, for more detail about the filter)

7. Image Preprocessing Cont.
Color Features
Convert RGB to HSV
HSV  to the way humans tend to perceive color
The value is in the range of 0..1
Quantize the image into 54 bins i.e. (6 x 3 x 3) bins
Record the frequency of the HSV of each pixel in the images

8. Image Preprocessing Cont.
Texture Features
Transform the images into frequency domain using the 8 filters generated (4 orientations and 2 scales parameters) and record the standard deviation and the mean of the pixel in the image after transformation
This process will produce 16 texture features for each image

9. Overview of SMART-TV Compute Root Counts Store the root count
Measure TV of each object in each class
Large
Training Set
Store the root count
and TV values
Preprocessing Phase
Unclassified Object
Approximate the candidate set of NNs
Search the K-nearest neighbors from the candidate set
Vote
Classifying Phase

10. SMART-TV Algorithm
SMART-TV: SMall Absolute diffeRence of ToTal Variation
Approximates a set of candidates of nearest neighbors by examining the absolute difference between the total variation of each data object in the training set and the total variation of the unclassified object
The k-nearest neighbors are searched from the candidate set
Computing Total Variation (TV):

11. Total Variation
The Total Variation of a set X about (the mean), , measures total squared separation of objects in X about , defined as follows:
TV(X,)=TV(X,x33) 1 2 3 4 5 X TV g
a- a  Y

12. SMART-TV Algorithm

13. The Independency of RC
The root count operations are independence from , which allows us to run the operations once in advance and retain the count results
In classification task, the sets of classes are known and unchanged. Thus, the total variation of an object about its class can be pre-computed

14. Preprocessing Phase Preprocessing:
The computation of root counts of each class Cj, where 1  j  number of classes.
O(kdb2) where k is the number of classes, d is the total of dimensions, and b is the bit-width
Compute , 1 j  number of classes. O(n) where n is the number of images in the training set

15. Classifying Phase Classifying:
For each class Cj, where 1  j  number of classes do:
a. Compute , where is the feature of the unclassified image
Find hs images in Cj such that the absolute difference between the total variation of the images in Cj and the total variation of are the smallest, i.e.
Let A be an array and , where
c. Store the ID of the images in an arrayTVGapList

16. Classifying Phase (Cont.)
For each objectIDt, 1 t  Len(TVGapList) where Len(TVGapList) is equal to hs times the total number of classes, retrieve the corresponding object features from the training set and measure the pair-wise Euclidian distance between and , i.e.
and determine the k nearest neighbors of
Vote the class label for from the k nearest neighbors

17. Dataset We used Corel images (http://wang.ist.psu.edu/docs/related)
10 categories
Originally, each category has 100 images
Number of feature attributes 70 (54 from color and 16 from texture)
We randomly generated several bigger size datasets to evaluate the speed and scalability of the algorithms
50 images for testing set, 5 for each category

18. Dataset Cont.

19. Experimental Results
Experimental Setup : Intel P4 CPU 2.6 GHz machine, 3.8GB RAM running Red Hat Linux
Classification Accuracy Comparison
Class
SMART-TV
KNN
k=3
k=5
k=7
hs=15
hs=25
hs=35 C1
0.69
0.72
0.75
0.74
0.73
0.78
0.81
0.77
0.79 C2
0.64
0.60
0.59
0.62
0.68
0.63
0.66 C3
0.65
0.67
0.76
0.57
0.70 C4
0.84
0.87
0.90
0.88 C5
0.91
0.92
0.93
0.89
0.94 C6
0.61
0.71 C7
0.85 C8
0.96 C9
0.52
0.43
0.45
0.54
C10
0.82

20. Example on Corel Dataset

21. Experimental Results Cont.
Loading Time
Classification Time

22. Summary
We have presented the SMART-TV algorithm, a classification algorithm that uses vertical data structure, and applied it in image classification task
We found that the speed of our algorithm outperforms the speed of the classical KNN algorithm
Our method scales well to large image repository. Its classification accuracy is very comparable to that of KNN algorithm