Image Retrieval using Color Moment Invariant

Xiaoyin Duanmu Technique and Quality Department Nanjing Electrical Equipment Ltd. 78 Changle Road, Nanjing, China

duanmuxiaoyin@yahoo.com

Abstract—This paper describes a new color image retrieval method using color moment invariant. The representative colors are computed from each image instead of being fixed in a given color space, thus allowing the feature representation to be accurate as well as compact. Different from previous methods, the proposed method uses small image descriptors and it is adaptive to the context of the image itself by a two-stage clustering technique. Experiments on the image library COIL-100 demonstrate the effectiveness of this method.

Color Moment, Invariant, Image Retrieval

I. INTRODUCTION With the fast development of the multimedia technique,

digital library and multimedia database including kinds of image databases increase rapidly, so it is exigent to efficiently organize, administer and retrieve image database with large scale. Traditional retrieval methods based on key words and description text can hardly satisfy the demanding requests from customers, therefore some scholars put forward Content-Based Image Retrieval (CBIR) as a new research topic two decades ago. Nowadays, CBIR has become a heavily investigate field (we refer the readers to [1] as a survey). CBIR is a method of combining digital image processing technique with database technique to retrieve image by using the color, shape and texture feature. Color feature is one of the most obvious characteristics of a color image, and it can be used for color images in any format. Many image retrieval methods using color feature have been proposed recently such as [2-4], the most common color feature extraction method is color histogram, and the most common methods used for calculating the similarity between two images are histogram intersection method, and distance method [5]. It is critical to enhance both the retrieval efficiency and accuracy from a huge image database.

We propose an adaptive image retrieval method, incorporated with image color invariants. Experiments show that, this method has improved simplicity and compactness without the loss of efficiency and robustness. The rest of the manuscript is organized as follows: Section 2 describes the proposed image descriptor; Section 3 presents experimental results; Section 4 draws conclusions.

II. DETECTING HOMOGENEOUS REGION BY CLUSTERING Color cue is very important for computer vision

applications, since it provides category independent information which severs as a prior for high-level reasoning, such as object recognition and image retrieval. Several successful retrieval theories have been developed over the past few years using color information. Among all these methods, the robustness with respect to changing illuminations remains a challenge. Even though color invariants are widely used to capture image features for its robustness to illumination conditions, it happens in practice that many kinds of color invariants are meaningless at particular positions in color space. The normalized colors are defined as

r=R/(R+G+B), g=G/(R+G+B), b=B/(R+G+B). Where, R, G, B are values from three channels of color images respectively.

It can be investigated that the normalized color are not stable near the black point, where small perturbation of image RGB values will cause large disruption in normalized color values. To solve this problem, a threshold is predefined. If the sum of RGB values at one point is less than the threshold, its color invariants of three channels are all set to zero. Theoretically speaking, there are only two independent parameters from the normalized color. However, these three normalized colors are all employed to construct the feature map, taking account of the block color affect. For many existing clustering algorithms, the number of clusters and initial means should be predefined, such as K-means. They are not suitable for general image retrieval, because the actual number of clusters varies from image to image, depending on the image contents. In order to solve this problem, the HAC is adopted in this paper. The HAC treats every block as a potential cluster and then successively agglomerate pairs of clusters, according to the similarity distance between clusters.

III. SIMILARITY MEASUREMENT The states of images are decided according to the image

contents, so the number of states and state feature values may vary from image to image. To match images with different number of states and different state feature values, the correspondences between image states are required.

2010 Seventh International Conference on Information Technology

978-0-7695-3984-3/10 $26.00 © 2010 IEEE

DOI 10.1109/ITNG.2010.231

200

Given two images represented by their parameter sets Θ1 and Θ2, the similarity measurement is performed as follows:

Step 1: The correspondences between the states of the two images are set up. The states of the two images are matched by the state feature values.

|| ||i jA BE V V= − (5)

where VA and VB are the state feature values of the two images. The i-th state of image A is considered to be matched with the j-th state of image B, if the corresponding E is the minimal for all js.

Step 2: The cooccurrence matrices of the two images are constructed. The cooccurrence matrix can be viewed as the sub-division of state transition matrix, consisting of rows and columns where the matched states lie. Given image A containing m image blocks a1,…,am, having m corresponding states with image B, the cooccurrence matrix T=(tij)m×m is defined as follows

1

ijij m

ikk

ct

c=

=∑

(6)

where C=(cij)L×L is the state transition matrix. The cooccurrence matrix element tij denotes the probability that the j-th state appears around the i-th state. Notice that the cooccurrence matrix only focuses on spatial relation between corresponding states, which is different from other CBIR methods.

IV. COLOR MOMENT INVARIANTS Once homogeneous regions are available, proper

descriptor for these regions will be needed. In general, scene retrieval system includes two main steps: image description and similarity measurement. Images usually have been described in feature space according to the color information or the texture properties, and then similarity measurement will be carried out between descriptors. However, descriptors used in many methods quantize images with fixed distance in the whole color space, and a hard decision is made about the structure of descriptors. Accordingly, these descriptors tend to have very large structures to deal with general images, which sometimes is unnecessary. In fact, a histogram with 1000 bins is used to depict the original image in figure1, and more than 95% pixels occupy less than 24 bins. Moreover, it often happens that different bins contain similar color or distinct colors appear in the same bins, which is caused by the fixed quantization. The other drawback for these methods is that the color distribution and spatial information are considered separately, instead of being combined to depict the image.

Moment invariants have become a classical tool for recognition since they were proposed in 1962. They are one of the most important shape descriptors, even if they suffer from some essential limitations. For example, they can not deal with the occlusion very well. Color moment invariants

combine shape and color information, so they are effective under changing illumination conditions.

V. EXPERIMENTS We conduct experiments on the object image library

COIL-100 of Columbia University, which contains 7200 pictures of 100 objects collected at Columbia University. The objects were placed on a motorized turntable against black background. The turntable was rotated through 360 degrees to vary object pose with respect to a fixed color camera. Images of the objects were taken at pose intervals of 5 degrees. This corresponds to 72 poses per object. 6 objects with 72 pictures per object plus 168 other pictures randomly picked constitute our experiment data. Some example pictures are shown in Figure 2 and 3.

Three state-of-the-art CBIR approaches are tested as a comparison: Hoi et al.’s Semi-Supervised Support Vector Machine Batch Mode Active Learning method (SS-SVM-BMAL) [7], Brinker’s SVM Active Learning with Diversity (SVM-ALD) [8], and Sajjanhar’s Semantic Color Names (SCN) [9]. Average precision and recall rates are shown in Figure 4 and 5, from which, we can draw the conclusion that average precision and recall rates of our method is better than the other three and that our method has the best distinguish ability.

VI. CONCLUSION In this paper, we propose a new color image retrieval

method using color moment invariant. Experiments show that, the proposed method is comparable to state-of-the-art image retrieval approaches.

REFERENCES

[1] Veltkamp, R.C. and Tanase, M. Content-Based Image Retrieval Systems: A Survey. Technical Report UU-CS-2000-34, 2000.

[2] Sun, J.D., Zhang, X.M. and Cui, J.T. Image retrieval based on color distribution entropy. Pattern Recognition Letters, 27(10): 1122-1126, 2006.

[3] Zhong, D. and Defe´e, I. DCT histogram optimization for image database retrieval. Pattern Recognition Letters, 26(14): 2272-2281, 2005.

[4] Nezamabadi-pour, H. and Kabir, E. Image retrieval using histograms of uni-color and bi-color blocks and directional changes in intensity gradient. Pattern Recognition Letters, 25(14): 1547-1557, 2004.

[5] Zhong, D. and Defe´e, I. Performance of similarity measures based on histograms of local image feature vectors. Pattern Recognition Letters, 2007, 28(15): 2003-2010

[6] Hastie, T., Tibshirani, R., and Friedman, J.H. The Elements of Statistical Learning. Springer, New York, 2001.

[7] Hoi, S.C.H., Jin, R., Zhu, J. and Lyu, M.R. Semi-supervised SVM batch mode active learning for image retrieval. Proceedings of CVPR 2008.

[8] Brinker, K. Incorporating diversity in active learning with support vector machines. Proceedings of ICML 2003.

[9] ajjanhar, A., Lu, G.J. and Zhang, D.S. Image retrieval based on semantics of intra-region color properties. 8th IEEE International Conference on Computer and Information Technology.

201

Figure 2. Images of one object with different poses

(a) Object 1 (b) Object 2 (c) Object 3

(d) Object 4 (e) Object 5 (f) Object 6

Figure 3. Sample objects

202

Figure 4. Average precision for the SS-SVM-BMAL method, the SVM-ALD method, the SCN method, and our method

Figure 5. Average recall rates for the SS-SVM-BMAL method, the SVM-ALD method, the SCN method, and our method

203