Content-Based Image Retrieval using Multi Deep Neural Networks and K-Nearest Neighbor Approaches

Abstract

Abstract With the rapid development of digital image technology, the number of digital images stored on the Internet environment has been increasing remarkably over the past decade. As a result, it has become a top priority to come up with an effective and convenient search tools for images. Although several image-based search tools have been introduced till now to allow users to search for images with relatively fast response times, it remains certain limitations from these tools in resolving ambiguity between content of the query image and returned image. In this research, we proposed a method exploiting multi deep neural networks and K-Nearest Neighbor methodologies in Content Based Image Retrieval to improve the image search quality. First, we built our own multi CNN models inherited from the pre-trained CNN models to extract image features. Next, basing on the method of K-Nearest Neighbors, we compiled the similarity measure of the distance between the feature vectors and conducted model experiments. Experiments were performed on the Oxford-IIIT Pet Image Dataset and self-collected dataset of a Kaggle competition. We then evaluated the model by accuracy, confusion matrix, and F1 scores; average precision (AP) and mean average precision (mAP) were used to evaluate the search results of our search proposed system. The experiments show that our proposed system achieved outstanding results.