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Traditional Bag-of-visual Words (BoWs) model is commonly generated with manysteps including local feature extraction, codebook generation, and featurequantization, etc. Those steps are relatively independent with each other andare hard to be jointly optimized. Moreover, the dependency on hand-craftedlocal feature makes BoWs model not effective in conveying high-level semantics.These issues largely hinder the performance of BoWs model in large-scale imageapplications. To conquer these issues, we propose an End-to-End BoWs(E$^2$BoWs) model based on Deep Convolutional Neural Network (DCNN). Our modeltakes an image as input, then identifies and separates the semantic objects init, and finally outputs the visual words with high semantic discriminativepower. Specifically, our model firstly generates Semantic Feature Maps (SFMs)corresponding to different object categories through convolutional layers, thenintroduces Bag-of-Words Layers (BoWL) to generate visual words for eachindividual feature map. We also introduce a novel learning algorithm toreinforce the sparsity of the generated E$^2$BoWs model, which further ensuresthe time and memory efficiency. We evaluate the proposed E$^2$BoWs model onseveral image search datasets including CIFAR-10, CIFAR-100, MIRFLICKR-25K andNUS-WIDE. Experimental results show that our method achieves promising accuracyand efficiency compared with recent deep learning based retrieval works.

E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network