Most existing matching algorithms are one-off algorithms, i.e., they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i.e., image matching, by recursively looking at specific related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.
Two key components are included in image matching, one is extracting proper features from the original image and the other is measuring the distance of the features to describe the similarity of images. At first, hand-craft features such as SIFT @cite_9 and DAISY @cite_13 are cooperated with fixed metric method like Graph Model @cite_17 @cite_29 @cite_2 to match images. It means that the two parts of matching (extracting feature and measuring similarity) are independent when using above methods and the isolation hinders the improvement of performance. To break the isolation, researchers propose learning descriptors or similarity metric in condition of fixed the other part. For example, @cite_6 learns the descriptors by minimizing the classification error and @cite_10 learns the metric by treating it as a linear transformation. Learning descriptors and metric jointly is proposed to make the cooperation of the two parts more powerful. @cite_21 , boosting trick is adopted to learn descriptors and metrics and achieved great performance. The performance of these methods are limited by the hand-crafted features, while the proposed method employs deep features and achieves better performance.
@cite_10 @cite_9 @cite_29 @cite_21 @cite_6 @cite_2 @cite_13 @cite_17
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We consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector. Popular representations of this kind include the bag-of-visual-words, the Fisher vector and the VLAD. When two such image representations are compared with the dot-product, the image-to-image similarity can be interpreted as a match kernel. In match kernels, one has to deal with interference , i.e., with the fact that even if two descriptors are unrelated, their matching score may contribute to the overall similarity. We formalise this problem and propose two related solutions, both aimed at equalising the individual contributions of the local descriptors in the final representation. These methods modify the aggregation stage by including a set of per-descriptor weights. They differ by the objective function that is optimised to compute those weights. The first is a “democratisation” strategy that aims at equalising the relative importance of each descriptor in the set comparison metric. The second one involves equalising the match of a single descriptor to the aggregated vector. These concurrent methods give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks. Matching images and sentences demands a fine understanding of both modalities. In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space. In this field, most existing works apply the ranking loss to pull the positive image text pairs close and push the negative pairs apart from each other. However, directly deploying the ranking loss is hard for network learning, since it starts from the two heterogeneous features to build inter-modal relationship. To address this problem, we propose the instance loss which explicitly considers the intra-modal data distribution. It is based on an unsupervised assumption that each image text group can be viewed as a class. So the network can learn the fine granularity from every image text group. The experiment shows that the instance loss offers better weight initialization for the ranking loss, so that more discriminative embeddings can be learned. Besides, existing works usually apply the off-the-shelf features, i.e., word2vec and fixed visual feature. So in a minor contribution, this paper constructs an end-to-end dual-path convolutional network to learn the image and text representations. End-to-end learning allows the system to directly learn from the data and fully utilize the supervision. On two generic retrieval datasets (Flickr30k and MSCOCO), experiments demonstrate that our method yields competitive accuracy compared to state-of-the-art methods. Moreover, in language based person retrieval, we improve the state of the art by a large margin. The code has been made publicly available. Recent methods for learning similarity between images have presented impressive results in the problem of pair matching (same notsame classification) of face images. In this paper we explore how well this performance carries over to the related task of multi-option face identification, specifically on the Labeled Faces in the Wild (LFW) image set. In addition, we seek to compare the performance of similarity learning methods to descriptor based methods. We present the following results: (1) Descriptor-Based approaches that efficiently encode the appearance of each face image as a vector outperform the leading similarity based method in the task of multi-option face identification. (2) Straightforward use of Euclidean distance on the descriptor vectors performs somewhat worse than the similarity learning methods on the task of pair matching. (3) Adding a learning stage, the performance of descriptor based methods matches and exceeds that of similarity methods on the pair matching task. (4) A novel patch based descriptor we propose is able to improve the performance of the successful Local Binary Pattern (LBP) descriptor in both multi-option identification and same not-same classification. The methodology for finding the same individual in a network of cameras must deal with significant changes in appearance caused by variations in illumination, viewing angle, and a person’s pose. Re-identification requires solving two fundamental problems: 1) determining a distance measure between features extracted from different cameras that cope with illumination changes (metric learning) and 2) ensuring that matched features refer to the same body part (correspondence). Most metric learning approaches focus on finding a robust distance measure between bounding box images, neglecting the alignment aspects. In this paper, we propose to learn appearance measures for patches that are combined using deformable models. Learning metrics for patches avoids strong dimensionality reduction, thus keeping more information. Additionally, we allow patches to change their locations, directly addressing the correspondence problem. As patches from different locations may share the same metric, our method effectively multiplies the amount of training data and allows patch metrics to be learned on the smaller amounts of labeled images. Different metric learning approaches (KISSME, XQDA, and LSSL) together with different deformable models (spring constraints and one-to-one matching constraints) are investigated and compared. For describing patches, we propose to learn a deep feature representation with convolutional neural networks, thus obtaining highly effective features for re-identification. We demonstrate that our approach significantly outperforms state-of-the-art methods on multiple data sets.