Image Retrieval Using Weighted Color Co-occurrence Matrix* Dong Liang, Jie Yang, Jin-jun Lu, and Yu-chou Chang Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, China Abstract. Weighted Color Co-occurrence Matrix (WCCM) is introduced as a novel feature for image retrieval. When indexing images with WCCM feature, the similarities of diagonal elements and non-diagonal elements are weighted respectively based on the Isolation Parameters of the query and prototype images. After weighting, the similarity of relevant matches to the query image is strengthened and the similarity of non-relevant matches to the query is weakened. The experiments show the effectiveness of WCCM based method.
1 Introduction Color Co-occurrence Matrix (CCM) [4-7] is a kind of commonly used color feature representation in image retrieval, but indexing image with CCM feature will ignore the shape information. Modified Color Co-occurrence Matrix (MCCM)  was proposed to overcome this disadvantage, where the similarities of diagonal elements and non-diagonal elements are taken into account respectively with equal weights. However, equally weighting on the similarities of homogeneous regions and nonhomogeneous regions is not a good choice. For example, if a query and a prototype consist of few homogeneous regions, the similarity of homogeneous regions should play a more important role in similarity measurement, when they all consist of many small regions, the similarity of non-homogeneous regions should play a more important role. In this paper, Weighted Color Co-occurrence Matrix (WCCM) is proposed as an image feature. In which, the similarities of homogeneous regions and nonhomogeneous regions of CCM are assigned with different weights based on the visual complexity of the query and prototype image. The rest of this paper is organized as follows. Section 2 describes the WCCM feature. Experimental results are shown in Section 3 and conclusion is made in Section 4.
2 Isolation Parameter and Weighted CCM Feature Let M be a co-occurrence matrix of image I, the MCCM feature vector is given by:
F I = ( M DI , M NI )
where M DI and M NI are diagonal elements and non-diagonal elements of CCM respectively. The similarity between the query Q and prototype I is:
S MCCM ( Q, I ) = 0.5S1 ( Q, I ) + 0.5S 2 ( Q, I )
Project supported by Key Technologies R&D Program of Shanghai (03DZ19320).
M. Jackson et al. (Eds.): BNCOD 2005, LNCS 3567, pp. 161–165, 2005. © Springer-Verlag Berlin Heidelberg 2005
Dong Liang et al.
where S1 ( Q, I ) and S 2 ( Q, I ) are the similarity of diagonal and non-diagonal elements respectively. We can see that for MCCM feature, the similarities of diagonal elements and non-diagonal elements are given same weights that mean the visual complexity of image is not considered. Here we propose weighted CCM (WCCM) feature for image retrieval. In matching stage, different weights are assigned on the similarity of homogeneous region and non-homogeneous region based on the visual complexity of image content, which is denoted by Isolation Parameter : N
pk = ∑ U k ( i ) N , U k ( i ) = N s ( f ( j ) = f ( i ) ) j ≠i N k
where pk ∈ ( 0,1) , N is the total number of pixels in image, k is the size of template, in our experiments k = 0.01× N . N k is the total number of pixels in k-neighbors of
pixel ( i ) , N s ( f ( j ) = f ( i ) ) j ≠ i indicates the number of pixel which has the same value as pixel ( i ) in k-neighbors, U k ( i ) ∈ ( 0,1) .
If k is defined, Isolation Parameter is only relevant to image visual complexity. It is small when image consists of many small regions, and big when image consists of few homogeneous regions. Fig.1 shows the Isolation Parameters of different images, from right to left, the image becomes more intricate, and the Isolation Parameter becomes smaller. From this figure, we can see that Isolation Parameter is in correspondence with the complexity of human visual perception.
Fig. 1. The Isolation Parameters of different images. (a)0.207, (b)0.386, (c)0.578, (d)0.915
In matching stage of WCCM, the similarity between the query Q and prototype I is:
S WCCM ( Q, I ) = w1S1 ( Q, I ) + w2 S 2 ( Q, I )
w1 and w2 are obtained based on the Isolation parameters of image Q and image I . In
this paper, a threshold pT = 0.5 is defined. If pk ≥ pT , we think image consisting of few homogeneous regions and if pk < pT , we think image consisting of many small regions. There are three instances: • pkQ ≥ pT and pkI ≥ pT , we strengthen the similarity of homogeneous region:
w1 = 2 − abs ( pkI − pkQ ) , w2 = 1
Image Retrieval Using Weighted Color Co-occurrence Matrix
We can see that the closer between pkQ and pkI , the bigger of w1 , and then the
bigger of w1 S1 ( Q, I ) , thus S WCCM ( Q, I ) > S MCCM ( Q, I ) , which means that the pro-
totype I becomes more relevant to the query Q on WCCM feature than on MCCM feature. • pkQ < pT and pkI < pT , we strengthen the similarity of non-homogeneous region:
w1 = 1 , w2 = 2 − abs ( pkI − pkQ )
In the same way, S WCCM ( Q, I ) > S MCCM ( Q, I ) , which also means that I becomes more relevant to the query Q on WCCM feature than on MCCM feature. • pkQ ≥ pT and pkI < pT or pkQ < pT and pkI ≥ pT , we weak the similarities of both homogeneous region and non homogeneous region:
w1 = w2 = 1/ 1 + abs ( pkI − pkD )
We can see that w1 and w2 are less than 1, S WCCM ( Q, I ) < S MCCM ( Q, I ) , I becomes more non-relevant to the query Q on WCCM feature than on MCCM feature when they have different content complexity. From the analysis above, we can see that the weighting is like a non-linear mapping. After weighting, the prototype images that have the similar color and visual complexity become more relevant to the query image and the images with the different color and visual complexity become more non-relevant to the query image.
3 Experimental Results The image database used consists of 2103 images, which was collected from the Internet and Corel dataset, the commonly used image database in image retrieval [1-3, 10-12]. The database has 60 semantic categories, each category consisting of 11-60 images. In this paper, the HSV color space is used and color (hue) is quantized to 16 colors because 16 bins are sufficient for proper color invariant object retrieval empirically . When indexing image, we randomly select images from each category as the queries, and return top 11 images to the user. Retrieval accuracy  and Averageretrieval-rank  are used as the performance criteria. In order to demonstrate the effectiveness for WCCM algorithm, we compare WCCM based method with MCCM based method  and SCH based method that is superior to the cumulative histogram and Color Moments . Fig.2 shows the performances using different features. In this figure for two criteria, WCCM based method outperforms the other features based methods. Fig.3 gives retrieval results for one query with different features. In which, SCH, MCCM, WCCM based methods return 4, 8, 9 relevant images respectively. We can see that non-relevant match of the third and eighth positions in MCCM based method are pushed back to the fourth and eleventh positions in retrieval result of WCCM based method, and non-relevant match of the ninth position is pushed out of top eleven. At the same time, relevant match of the eleventh and tenth positions in MCCM based method are pushed forward to the third and sixth positions in WCCM
Dong Liang et al.
based method, and one relevant match out of the top eleven in MCCM based method is pushed forward to the seventh position. We can see from fig.3 that some relevant images become more relevant to the query on WCCM feature than on MCCM feature and instead, some non-relevant images become more non-relevant on WCCM feature than on MCCM feature.
Fig. 2. The performance using different features
Fig. 3. Retrieval results using different features. (left for SCH, middle for MCCM and right for WCCM, the top-left is the query image)
4 Conclusion A novel feature Weighted Color Co-occurrence Matrix (WCCM) is proposed. When indexing images, the similarity of homogeneous region and non- homogeneous region are assigned different weights based on the Isolation Parameters of the query and prototype. After weighting, relevant matches become more relevant and non-relevant matches become more non-relevant. The experiments show the superiority of proposed feature in comparison with MCCM and SCH feature.
References 1. Y.Rui, T.S.Huang, M.Ortega, S.Mehrotra, Relevance feedback: a power tool for interactive content-based image retrieval, IEEE Trans. On Circuits and Systems for Video Technology, 8(5), (1998) 644-655. 2. M.Flickner, J.Sawhney, etc.: Query by Image and Video Content: the QBIC system, IEEE computer. Vol. 28, (1995) 23-32
Image Retrieval Using Weighted Color Co-occurrence Matrix
3. Xiaofei He, Oliver King, Wei-Ying Ma, Mingjing Li, Hong-Jiang Zhang, Learning a Semantic Space From User’s Relevance Feedback for Image Retrieval, IEEE Trans. On Circuits and Systems for Video Technology, 13(1), (2003) 39-48. 4. V. Kovalev, S. Volmer.: Color Co-occurrence Descriptor for Querying-by-Example. Multimedia Modeling. (1998) 32-38. 5. Qiu, Guoping, Color image indexing using BTC, IEEE Transactions on Image Processing 12(1), (2003) 93-101. 6. Qiu, G., Constraint adaptive segmentation for color image coding and content-based retrieval, 2001 IEEE Fourth Workshop on Multimedia Signal Processing (2001) 269-274. 7. Seong-O Shim, Tae-Sun Choi.: Image Indexing by Modified Color Co-occurrence Matrix. IEEE International Conference on Image Processing. Vol 3, (2003) 493-496. 8. Luo Yun, Zhang Yu-Jin, Gao Yong-Ying.: Meaningful Regions Extraction Based on Image Analysis. CHINESE JOURNAL of COMPUTER. 23(12), (2000) 1313-1319. 9. Zhang Y J, Liu Z W, He Y.: Color-based Image Retrieval using Sub-range Cumulative Histogram. High Technology Letters. 4(2), (1998) 71-75. 10. Gevers, T. Smeulders, A.W.M. PicToSeek: combining color and shape invariant features for image retrieval, IEEE Transactions on Image Processing, 9(1), (2000) 102-119. 11. Ko, Byoungchul Byun, Hyeran, Extracting salient regions and learning importance scores in region-based image retrieval, International Journal of Pattern Recognition and Artificial Intelligence, 17(8), (2003) 1349-1367. 12. Hoiem, Derek Sukthankar, Rahul; Schneiderman, Henry; Huston, Larry, Object-based image retrieval using the statistical structure of images, Proceedings of the 2004 CVPR, V2, (2004) 490-497.