# Color-Based Image Retrieval Using Proximity Space Theory
## 3 Experiment and Results  
In this section, experimental results of the proposed method on COIL-20 [7] image database and Corel-1000 [10] database are presented. The COIL-20 database involves 1440 images, in which the images are separated into twenty categories (see Figure 4), and every category includes 72 images. The size of each image is \(128\times 128\). Table 1 shows the results of enhanced-surf method and our method, respectively, on COIL-20 database. \(O_{R}\) is the number of relevant images retrieved, while \(O_{T}\) is the total number of images retrieved. Thus, the precision and the recall of this retrieval system are given by Equation(11). \[Precision=\frac{O_{R}}{O_{T}},\;\;Recall=\frac{O_{R}}{72}. \tag{11}\]  
According to the results in Table 1b, we can get the average precision is 0.4818 and the average recall is 0.8387 by calculating the mean value of the third and fourth line, respectively. Compared with the enhanced SURF, these results demonstrate that the average precision and the average recall of our method increase by 16.77% and 28.61%, respectively. Figure 4: Images selected from COIL-20 dataset. Figure 3: The left image shows car category results, while the right image shows cup category results. The Corel-1000 database includes 1000 images from ten categories (African tribes, Beaches, Buildings, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains, and Foods) (see Figure 5). Each category contains 100 images with size of 384 \(\times\) 256 or 256 \(\times\) 384. Each image in this database is taken to query. Table 2 shows the precision and recall results of our proposed method compared with CLD [27], Color Moment [28] and CSD [29]. Precision is computed from the Top-10 retrieved images and recall is calculated from the Top-100 retrieved images. CLD takes spatial information into account, but fails to find rotated or translated images that are similar to query images. Color moment has nine color components, with three low-order moments for each color channel, but low-order moments are less discriminating, so it is often used in combination with other features. CSD is mainly used for static image retrieval, and only considers whether the structure contains a certain color, and not about its frequency. Table 2 shows that the average precision of our experimental results are 17.4%, 16.1% and 5.6% higher than CLD, color moment and CSD, respectively; the average recall of our experimental results are 9.4%, 5.5% and 2.5% higher than CLD, color moment and CSD, respectively; and our method retrieves buildings category images, the elephants category images and foods category images better than the other algorithms. For example, buildings images can be more precisely retrieved by using our method, and it can be seen that the average precision of our experimental results are 32.3%, 28.2% and 11.1% higher than CLD, color moment and CSD, respectively; and the average recall of our experimental results are 24%, 18.6% and 5.6% higher than CLD, color moment and CSD, respectively. The precision of dinosaurs category and flower category are highly retrieved in our proposed algorithm.