Click Feature Research Articles

Fine-grained image classification with factorized deep user click feature

The advantages of user click data greatly inspire its wide application in fine-grained image classification tasks. In previous click data based image classification approaches, each image is represented as a click frequency vector on a pre-defined query/word dictionary. However, this approach not only introduces high-dimensional issues, but also ignores the part of speech (POS) of a specific word as well as the word correlations. To address these issues, we devise the factorized deep click features to represent images. We first represent images as the factorized TF-IDF click feature vectors to discover word correlation, wherein several word dictionaries of different POS are constructed. Afterwards, we learn an end-to-end deep neural network on click feature tensors built on these factorized TF-IDF vectors. We evaluate our approach on the public Clickture-Dog dataset. It shows that: 1) the deep click feature learned on click tensor performs much better than traditional click frequency vectors; and 2) compared with many state-of-the-art textual representations, the proposed deep click feature is more discriminative and with higher classification accuracies.

Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition.

The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained image recognition. Unfortunately, user click frequency data are usually absent in practice. It remains challenging to predict the click feature from the visual feature, because the user click frequency vector of an image is always noisy and sparse. In this paper, we devise a Hierarchical Deep Word Embedding (HDWE) model by integrating sparse constraints and an improved RELU operator to address click feature prediction from visual features. HDWE is a coarse-to-fine click feature predictor that is learned with the help of an auxiliary image dataset containing click information. It can therefore discover the hierarchy of word semantics. We evaluate HDWE on three dog and one bird image datasets, in which Clickture-Dog and Clickture-Bird are utilized as auxiliary datasets to provide click data, respectively. Our empirical studies show that HDWE has 1) higher recognition accuracy, 2) a larger compression ratio, and 3) good one-shot learning ability and scalability to unseen categories.

Image Recognition by Predicted User Click Feature With Multidomain Multitask Transfer Deep Network.

The click feature of an image, defined as a user click count vector based on click data, has been demonstrated to be effective for reducing the semantic gap for image recognition. Unfortunately, most of the traditional image recognition datasets do not contain click data. To address this problem, researchers have begun to develop a click prediction model using assistant datasets containing click information and have adapted this predictor to a common click-free dataset for different tasks. This method can be customized to our problem, but it has two main limitations: 1) the predicted click feature often performs badly in the recognition task since the prediction model is constructed independently of the subsequent recognition problem and 2) transferring the predictor from one dataset to another is challenging due to the large cross-domain diversity. In this paper, we devise a multitask and multidomain deep network with varied modals (MTMDD-VM) to formulate image recognition and click prediction tasks in a unified framework. Datasets with and without click information are integrated in the training. Furthermore, a nonlinear word embedding with a position-sensitive loss function is designed to discover the visual click correlation. We evaluate the proposed method on three public dog breed image datasets, and we utilize the Clickture-Dog dataset as the auxiliary dataset that provides click data. The experimental results show that: 1) the nonlinear word embedding and position-sensitive loss function largely enhance the predicted click feature in the recognition task, realizing a 32% improvement in accuracy; 2) the multitask learning framework improves accuracies in both image recognition and click prediction; and 3) the unified training using the combined dataset with and without click data further improves the performance. Compared with the state-of-the-art methods, the proposed approach not only performs much better in accuracy but also achieves good scalability and one-shot learning ability.

User-Click-Data-Based Fine-Grained Image Recognition via Weakly Supervised Metric Learning

We present a novel fine-grained image recognition framework using user click data, which can bridge the semantic gap in distinguishing categories that are similar in visual. As query set in click data is usually large-scale and redundant, we first propose a click-feature-based query-merging approach to merge queries with similar semantics and construct a compact click feature. Afterward, we utilize this compact click feature and convolutional neural network (CNN)-based deep visual feature to jointly represent an image. Finally, with the combined feature, we employ the metriclearning-based template-matching scheme for efficient recognition. Considering the heavy noise in the training data, we introduce a reliability variable to characterize the image reliability, and propose a weakly-supervised metric and template leaning with smooth assumption and click prior (WMTLSC) method to jointly learn the distance metric, object templates, and image reliability. Extensive experiments are conducted on a public Clickture-Dog dataset and our newly established Clickture-Bird dataset. It is shown that the click-data-based query merging helps generating a highly compact (the dimension is reduced to 0.9%) and dense click feature for images, which greatly improves the computational efficiency. Also, introducing this click feature into CNN feature further boosts the recognition accuracy. The proposed framework performs much better than previous state-of-the-arts in fine-grained recognition tasks.

Click data guided query modeling with click propagation and sparse coding

We address the problem of fine-grained image recognition using user click data, wherein each image is represented as a semantical query-click feature vector. Usually, the query set obtained from search engines is large-scale and redundant, making the click feature be high-dimensional and sparse. We propose a novel query modeling approach to merge semantically similar queries, and construct a compact click feature with the merged queries. To deal with the sparsity and in-consistency in click feature, we design a graph based propagation approach to predict the zero-clicks, ensuring similar images have similar clicks for each query. Afterwards, using the propagated click feature, we formulate the query merging problem as a sparse coding based recognition task. In addition, the hot queries are utilized to construct the dictionary. We evaluate our method for fine-grained image recognition on the public Clickture-Dog dataset. It is shown that, the propagated click feature performs much better than the original one. In the query merging procedure, sparse coding performs better than traditional K-mean algorithm. Also, the “hot queries” outperform K-SVD in dictionary learning.

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.