Pre-trained CNN Features Research Articles

Unsupervised Content Based Image Retrieval Using Pre-Trained CNN and PCNN Features Extractors

Unsupervised Content Based Image Retrieval Using Pre-Trained CNN and PCNN Features Extractors

Prosperous Human Gait Recognition: an end-to-end system based on pre-trained CNN features selection

Human Gait Recognition (HGR) is a biometric approach, widely used for security purposes from the past few decades. In HGR, the change in an individual walk along with wearing clothes and carrying bag are major covariant controls which impact the performance of a system. Moreover, recognition under various view angles is another key challenge in HGR. In this work, a novel fully automated method is proposed for HGR under various view angles using deep learning. Four primary steps are involved such as: preprocessing of original video frames, exploiting pre-trained Densenet-201 CNN model for features extraction, reduction of additional features from extracted vector based on a hybrid selection method, and finally recognition using supervised learning methods. The extraction of CNN features is a key step in which our target is to extract the most active features. To achieve this goal, we fuse the features of both second last and third last layers in a parallel process. At a later stage, best features are selected by the Firefly algorithm and Skewness based approach. These selected features are serially combined and fed to One against All Multi Support Vector Machine (OAMSVM) for final recognition. Three different angles 180, 360 and 540 of the CASIA B dataset are selected for the evaluation process and accuracy of 94.3%, 93.8% and 94.7% is achieved respectively. Results show significant improvement in accuracy and recall rate as compared to the existing state-of-the-art techniques.

Cross-Domain Shoe Retrieval With a Semantic Hierarchy of Attribute Classification Network.

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the semantic hierarchy of attribute convolutional neural network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe data set composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.