Image Retrieval using CNN and Low-level Feature Fusion for Crime Scene Investigation Image Database

Abstract

Crime scene investigation (CSI) image retrieval is used to search for crime evidences and is critical in helping in solving various crimes. In recent years, using Convolutional Neural Network (CNN) has demonstrated outstanding performances in large-scale image database retrieval. However, to prevent over-fitting in the training of CNN model due to limited number of CSI images, this paper proposes to cascade two CNN models obtained based on transfer learning and combine CNN features with low-level image feature to better describe CSI images. First, two pre-trained CNN models are fine-tuned using the target image set. CNN features are extracted from fully connected layer of each model and are concatenated as high-level features for the image. These concatenated CNN features are then fused with the low-level image features of the target image set. The final fused image features are used in the image retrieval. Experimental results on CSI image database proved the effectiveness of the proposed algorithm for limited number of training sets. In addition, experiments carried out on the GHIM-10K database proved the generalizability of the proposed algorithm.