Ensembling of text and images using Deep Convolutional Neural Networks for Intelligent Information Retrieval

Information retrieval (IR) defines the process of searching and attaining specific information resources which are related to the specific information requirements from the available resource pool. It finds useful in several real time application areas namely digital library, healthcare, education, internet browsing, etc. Recently, deep learning (DL) models become popular in different fields of image processing, object detection, and natural language processing. Therefore, in this paper, DL models are employed to retrieve the text and images proficiently. This paper presents an ensemble of DL based IR models for text and images. The proposed model intends to develop DL models individually for text and images. Initially, convolutional neural network based VGGNet-19 model is used as a feature extractor and Euclidian distance based similarity measurement for the retrieval of images. At the same time, bidirectional-long short-term memory (BiLSTM) technique is applied for retrieval of textual documents. The presented BiLSTM model sequentially considers every word in a sentence, extracts the details and embeds it to the semantic vector. In addition to the feature extraction using deep learning techniques, the similarity measurement emphasis the closeness of the document to the given query. The proposed retrieval system has tested on text and images for both general and specific domain (agriculture) with the datasets of Yahoo, Google and Corel10K. With the datasets the performance has been computed by the standard measures such as precision, recall and F-score where the proposed deep learning model produces better results when compared to existing techniques. The proposed model has been tested for the specific domain and achieves the performance of 93% precision and 85% recall and 90% F-score when compared to the existing model.