Convolutional neural networks have been widely used in content-based image retrieval. To better deal with large-scale data, the deep hashing model is proposed as an effective method, which maps an image to a binary code that can be used for hashing search. However, most existing deep hashing models only utilize fine-level semantic labels or convert them to similar dissimilar labels for training. The natural semantic hierarchy structures are ignored in the training stage of the deep hashing model. In this paper, we present an effective algorithm to train a deep hashing model that can preserve a semantic hierarchy structure for large-scale image retrieval. Experiments on two datasets show that our method improves the fine-level retrieval performance. Meanwhile, our model achieves state-of-the-art results in terms of hierarchical retrieval.
Recently, the semantic hierarchy learning problem has been addressed in several works. The hierarchical semantic image retrieval model is proposed in @cite_14 . This work encodes hierarchy in semantic similarity. By combining the coarse and fine level labels, work in @cite_18 proves that the image classification performance can be improved. The similar idea has been shared in @cite_5 , which considers using a hierarchical training strategy to handle the face recognition task. Recent work in @cite_5 integrates the semantic relationship between class level into deep learning. The hierarchical similarity learning problem has also been addressed for deep hashing in SHDH @cite_21 . SHDH tackles the semantic hierarchy learning by proposing a weighted Hamming distance. However, SHDH also uses pair-wise relation, which has been proved not as efficient as using class-level labels @cite_17 @cite_12 .
@cite_18 @cite_14 @cite_21 @cite_5 @cite_12 @cite_17
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The performance of classifiers is in general improved by designing models with a large number of parameters or by ensembles. We tackle the problem of classification of coarse and fine grained categories, which share a semantic relationship. On being given the predictions that a classifier has for a given test sample, we adjust the probabilities according to the semantics of the categories, on which the classifier was trained. We present an algorithm for doing such an adjustment and we demonstrate improvement for both coarse and fine grained classification. We evaluate our method using convolutional neural networks. However, the algorithm can be applied to any classifier which outputs category wise probabilities. This paper addresses the problem of similar image retrieval, especially in the setting of large-scale datasets with millions to billions of images. The core novel contribution is an approach that can exploit prior knowledge of a semantic hierarchy. When semantic labels and a hierarchy relating them are available during training, significant improvements over the state of the art in similar image retrieval are attained. While some of this advantage comes from the ability to use additional information, experiments exploring a special case where no additional data is provided, show the new approach can still outperform OASIS [6], the current state of the art for similarity learning. Exploiting hierarchical relationships is most important for larger scale problems, where scalability becomes crucial. The proposed learning approach is fundamentally parallelizable and as a result scales more easily than previous work. An additional contribution is a novel hashing scheme (for bilinear similarity on vectors of probabilities, optionally taking into account hierarchy) that is able to reduce the computational cost of retrieval. Experiments are performed on Caltech256 and the larger ImageNet dataset. Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing hashing methods did not consider the hierarchical relation of labels, which means that they ignored the rich information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally, which does not meet the scenario of hierarchical labeled data. In this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data. Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each layer, and design a deep convolutional neural network to obtain a hash code for each data point. Extensive experiments on several real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task. Recent progress of face recognition benefits a lot from large-scale face datasets with deep Convoluitonal Neural Networks(CNN). However, when dataset contains a large number of subjects but with few samples for each subject, conventional CNN with softmax loss is heavily prone to overfitting. To address this issue, we propose a hierarchical training schema to optimize CNN with coarse-to-fine class labels, referred to as Hit-CNN. Firstly trained with coarse class labels and then refined with fine class labels, Hit-CNN is enabled the to capture the distribution of data from major variations to fine variations progressively, which can effectively relieve the overfitting and lead to better generalization. In this work, the hierarchical coarse-to-fine class labels are obtained via hierarchical k-means clustering according to the face identities. Evaluated on two face datasets, the proposed Hit-CNN provides better results compared with the conventional CNN under the circumstances of large-scale data with few samples per subject. Deep supervised hashing has emerged as an influential solution to large-scale semantic image retrieval problems in computer vision. In the light of recent progress, convolutional neural network based hashing methods typically seek pair-wise or triplet labels to conduct the similarity preserving learning. However, complex semantic concepts of visual contents are hard to capture by similar dissimilar labels, which limits the retrieval performance. Generally, pair-wise or triplet losses not only suffer from expensive training costs but also lack in extracting sufficient semantic information. In this regard, we propose a novel deep supervised hashing model to learn more compact class-level similarity preserving binary codes. Our deep learning based model is motivated by deep metric learning that directly takes semantic labels as supervised information in training and generates corresponding discriminant hashing code. Specifically, a novel cubic constraint loss function based on Gaussian distribution is proposed, which preserves semantic variations while penalizes the overlap part of different classes in the embedding space. To address the discrete optimization problem introduced by binary codes, a two-step optimization strategy is proposed to provide efficient training and avoid the problem of gradient vanishing. Extensive experiments on four large-scale benchmark databases show that our model can achieve the state-of-the-art retrieval performance. Moreover, when training samples are limited, our method surpasses other supervised deep hashing methods with non-negligible margins. This paper presents a simple yet effective supervised deep hash approach that constructs binary hash codes from labeled data for large-scale image search. We assume that the semantic labels are governed by several latent attributes with each attribute on or off , and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semantics-preserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network and the binary codes are learned by minimizing an objective function defined over classification error and other desirable hash codes properties. With this design, SSDH has a nice characteristic that classification and retrieval are unified in a single learning model. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a point-wised manner, and thus is scalable to large-scale datasets. SSDH is simple and can be realized by a slight enhancement of an existing deep architecture for classification; yet it is effective and outperforms other hashing approaches on several benchmarks and large datasets. Compared with state-of-the-art approaches, SSDH achieves higher retrieval accuracy, while the classification performance is not sacrificed.