To show the effectiveness of the proposed system, some experiments will be reported. Selecting a suitable image database is a critical and important step in designing an image retrieval system.
Figure 5 : Retrieval process. (a) Query image. (b) Retrieved results obtained by visual descriptors similarity criterion. Retrieved results after (c) the first, (d) the second, (e) the third, and (f) the fourth generation of IGA
At the present time, there is not a standard image database for this purpose. Also, there is no agreement on the type and the number of images in the database. Since most image retrieval systems are intended for general databases, it is reasonable to include various semantic groups of images in the database. In our experiments, we used the database of the Simplicity project covering a wide range of semantic categories from natural scenes to artificial objects for experiments.
The database is partitioned into ten categories, including African people and village, beach, buildings, buses, dinosaurs, elephants, flowers, horses, mountains and glaciers, food, etc., and each category contains 100 images (Fig. 4).
A. Practicability of System Demonstration
At first, we give an example to illustrate the practicability of our proposed system. A user submits an image containing a bus as the query image into the system, and then, the similarity measurement module of the system compares the query features with those images in the database and finds the most similar images to the query image. These images are ranked based on the similarity. Under each image, a slide bar is attached so that the user can tell the system which images are relevant or irrelevant. The amount of slider movement represents the degree of relevance. The user can repeat this process until he/she is satisfied with the retrieval results. Fig. 5 shows the first display of returned images and the retrieved results after applying the IGA process. The results are ranked in ascending order of similarity to the query image from left to right and then from top to bottom. From the results, we can find that if the retrieval only considers the low-level features, some irrelevant images are retrieved.
By adopting user’s subjective expectation, the retrieval results are effectively increased in very few generations.
B. Retrieval Precision/Recall Evaluation
To evaluate the effectiveness of the proposed approach, we examined how many relevant images to the query were retrieved.
The retrieval effectiveness can be defined in terms of precision and recall rates. Experiments are run five times, and average results are reported. In every experiment, an evaluation of the retrieval precision is performed so that ten images that were randomly selected from each specific category of the database are used as query images. For each query image, relevant images are considered to be those and only those which belong to the same category as the query image. Based on this concept, the retrieval precision and recall are defined as
where NA(q) denotes the number of relevant images similar to the query, NR(q) indicates the number of images retrieved by the system in response to the query, and Nt represents the total number of relevant images available in the database. When each precision and recall for ten images are obtained, we discard the best value and the worst one and then average these values to obtain the average precision and average recall. Figs. 6 and 7 show them, respectively. In Fig. 5, we observe that the tendency of average precision for the randomly selected images in each specific category is toward higher value with the proposed approach, and they can achieve 100% within few generations of IGA. The same phenomenon appears in Fig. 7.