Search

Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlations such as the same ancestors or similar habits, which are helpful in taxonomy and understanding biological traits. In this paper, we devise a new fine-grained retrieval task that searches for similar instances from different species based on body parts. To this end, we propose a two-step strategy. In the first step, we search for visually similar parts to a query image using a deep convolutional neural network (CNN). To improve the quality of the retrieved candidates, structural cues are introduced into the CNN using a novel part-pooling layer, in which the receptive field of each part is adjusted automatically. In the second step, we re-rank the retrieved candidates to improve the species diversity. We achieve this by formulating a novel ranking function that balances between the similarity of the candidates to the queried parts, while decreasing the similarity to the query species. We provide experiments on the benchmark CUB200 dataset and Columbia Dogs dataset, and demonstrate clear benefits of our schemes. [ABSTRACT FROM AUTHOR]

Facial action unit (AU) recognition has been applied in a wild range of fields, and has attracted great attention in the past two decades. Most existing works on AU recognition assumed that the complete label assignment for each training image is available, which is often not the case in practice. Labeling AU is expensive and time consuming process. Moreover, due to the AU ambiguity and subjective difference, some AUs are difficult to label reliably and confidently. Many AU recognition works try to train the classifier for each AU independently, which is of high computation cost and ignores the dependency among different AUs. In this work, we formulate AU recognition under incomplete data as a multi-label learning with missing labels (MLML) problem. Most existing MLML methods usually employ the same features for all classes. However, we find this setting is unreasonable in AU recognition, as the occurrence of different AUs produce changes of skin surface displacement or face appearance in different face regions. If using the shared features for all AUs, much noise will be involved due to the occurrence of other AUs. Consequently, the changes of the specific AUs cannot be clearly highlighted, leading to the performance degradation. Instead, we propose to extract the most discriminative features for each AU individually, which are learned by the supervised learning method. The learned features are further embedded into the instance-level label smoothness term of our model, which also includes the label consistency and the class-level label smoothness. Both a global solution using st-cut and an approximated solution using conjugate gradient (CG) descent are provided. Experiments on both posed and spontaneous facial expression databases demonstrate the superiority of the proposed method in comparison with several state-of-the-art works. [ABSTRACT FROM AUTHOR]

Interactions among pedestrians usually play an important role in understanding crowd behavior. However, there are great challenges, such as occlusions, motion, and appearance variance, on accurate analysis of pedestrian interactions. In this paper, we introduce a novel social attribute-aware force model (SAFM) for detection of abnormal crowd events. The proposed model incorporates social characteristics of crowd behaviors to improve the description of interactive behaviors. To this end, we first efficiently estimate the scene scale in an unsupervised manner. Then, we introduce the concepts of social disorder and congestion attributes to characterize the interaction of social behaviors, and construct our crowd interaction model on the basis of social force by an online fusion strategy. These attributes encode social interaction characteristics and offer robustness against motion pattern variance. Abnormal event detection is finally performed based on the proposed SAFM. In addition, the attribute-aware interaction force indicates the possible locations of anomalous interactions. We validate our method on the publicly available data sets for abnormal detection, and the experimental results show promising performance compared with alternative and state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Abstract: Visual codebook serves as a fundamental component in many state-of-the-art visual search and object recognition systems. While most existing codebooks are built based solely on unsupervised patch quantization, there are few works exploited image labels to supervise its construction. The key challenge lies in the following: image labels are global, but patch supervision should be local. Such imbalanced supervision is beyond the scope of most existing supervised codebooks [9,10,12–15,29]. In this paper, we propose a weakly supervised codebook learning framework, which integrates image labels to supervise codebook building with two steps: the Label Propagation step propagates image labels into local patches by multiple instance learning and instance selection [20,21]. The Graph Quantization step integrates patch labels to build codebook using Mean Shift. Both steps are co-optimized in an Expectation Maximization framework: the E-phase selects the best patches that minimize the semantic distortions in quantization to propagate image labels; while the M-phase groups similar patches with related labels (modeled by WordNet [18]), which minimizes the visual distortions in quantization. In quantitative experiments, our codebook outperforms state-of-the-art unsupervised and supervised codebooks [1,10,11,25,29] using benchmark datasets. [Copyright &y& Elsevier]

Abstract: We propose to learn an extremely compact visual descriptor from the mobile contexts towards low bit rate mobile location search. Our scheme combines location related side information from the mobile devices to adaptively supervise the compact visual descriptor design in a flexible manner, which is very suitable to search locations or landmarks within a bandwidth constraint wireless link. Along with the proposed compact descriptor learning, a large-scale, contextual aware mobile visual search benchmark dataset PKUBench is also introduced, which serves as the first comprehensive benchmark for the quantitative evaluation of how the cheaply available mobile contexts can help the mobile visual search systems. Our proposed contextual learning based compact descriptor has shown to outperform the existing works in terms of compression rate and retrieval effectiveness. [Copyright &y& Elsevier]

With the popularization of mobile devices, recent years have witnessed an emerging potential for mobile landmark search. In this scenario, the user experience heavily depends on the efficiency of query transmission over a wireless link. As sending a query photo is time consuming, recent works have proposed to extract compact visual descriptors directly on the mobile end towards low bit rate transmission. Typically, these descriptors are extracted based solely on the visual content of a query, and the location cues from the mobile end are rarely exploited. In this paper, we present a Location Discriminative Vocabulary Coding (LDVC) scheme, which achieves extremely low bit rate query transmission, discriminative landmark description, as well as scalable descriptor delivery in a unified framework. Our first contribution is a compact and location discriminative visual landmark descriptor, which is offline learnt in two-step: First, we adopt spectral clustering to segment a city map into distinct geographical regions, where both visual and geographical similarities are fused to optimize the partition of city-scale geo-tagged photos. Second, we propose to learn LDVC in each region with two schemes: (1) a Ranking Sensitive PCA and (2) a Ranking Sensitive Vocabulary Boosting. Both schemes embed location cues to learn a compact descriptor, which minimizes the retrieval ranking loss by replacing the original high-dimensional signatures. Our second contribution is a location aware online vocabulary adaption: We store a single vocabulary in the mobile end, which is efficiently adapted for a region specific LDVC coding once a mobile device enters a given region. The learnt LDVC landmark descriptor is extremely compact (typically 10-50 bits with arithmetical coding) and performs superior over state-of-the-art descriptors. We implemented the framework in a real-world mobile landmark search prototype, which is validated in a million-scale landmark database covering typical areas e.g. Beijing, New York City, Lhasa, Singapore, and Florence. [ABSTRACT FROM AUTHOR]