Abstract
Content Based Image Retrieval (CBIR) techniques are becoming an essential requirement in the multimedia systems with the widespread use of internet, declining cost of storage devices and the exponential growth of un-annotated digital image information available in recent years. Therefore multi query systems have been used rather than a single query in order to bridge the semantic gaps and in order to understand user’s requirements. Moreover, query replacement algorithm has been used in the previous works in which user provides multiple images to the query image set referred as representative images. Feature vectors are extracted for each image in the representative image set and every image in the database. The centroid, Crep of the representative images is obtained by computing the mean of their feature vectors. Then every image in the representative image set is replaced with the same candidate image in the dataset one by one and new centroids are calculated for every replacement .The distance between each of the centroids resulting from the replacement and the representative image centroid Crep is calculated using Euclidean distance. The cumulative sum of these distances determines the similarity of the candidate image with the representative image set and is used for ranking the images. The smaller the distance, the similar will be the image with the representative image set. But it has some research gaps like it takes a lot of time to extract feature of each and every image from the database and compare our image with the database images and complexity as well as cost increases. So in our proposed work, the KNN algorithm is applied for classification of images in the database image set using the query images and the candidate images are reduced to images returned after classification mechanism which leads to decrease the execution time and reduce the number of iterations. Hence due to hybrid model of multi query and KNN, the effectiveness of image retrieval in CBIR system increases. The language used in this work is C /C++ with Open CV libraries and IDE is Visual studio 2015. The experimental results show that our method is more effective to improve the performance of the retrieval of images.
Highlights
Content-Based Image Retrieval (CBIR) systems are search engines for image databases, which index images according to their content
The query image is converted into the internal representation of feature vector using the same feature extraction routine that was used for building the feature database
Every image in the representative image set is replaced with the same candidate image in the dataset one by one and new centroids are calculated for every replacement
Summary
Content-Based Image Retrieval (CBIR) systems are search engines for image databases, which index images according to their content. A typical task solved by CBIR system s is that a user subm its a query im age or series of im ages and the system is required to retrieve images from the database as similar as possible. Another task is a support for browsing through large image databases, where the images are supposed to be grouped or organized in accordance with similar properties. There are two main reasons, the first is so called semantic gap, which is the difference between information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. User’s relevance feedback is incorporated to further improve the retrieval process in order to produce perceptually and semantically more meaningful retrieval results
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