Personal Clothing Retrieval on Photo Collections by Color and Attributes

Wang, Xianwang; Zhang, Tong; Tretter, Daniel; Lin, Qian

doi:10.1109/tmm.2013.2279658

Cited by 52 publications

(18 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of the semantic nature of attribute‐based representations, in the recent years, they have attracted considerable attention and have been applied to multifarious applications, such as image retrieval (Kovashka et al, ; Zhang, Zha, Yan, Bian, & Chua, ; Wang, Zhang, Tretter, & Lin, ; Choi, ; Chen, Chen, Kuo, & Hsu, ; Siddiquie, Feris, & Davis, ; F. X. Yu, Ji, Tsai, Ye, & Chang, ; Douze, Ramisa, & Schmid, ), image recognition and segmentation (Lu, Yang, Yang, & Rui, ; Shi, Yang, Hospedales, & Xiang, ; Cho, Kang, & Kim, ), clothing recommendation (Liu et al, ), visual object tracking (Danelljan, Khan, Felsberg, & van de Weijer, ), and person re‐identification (C. Su et al, ).…”

Section: Related Workmentioning

confidence: 99%

PSI: A probabilistic semantic interpretable framework for fine‐grained image ranking

et al. 2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine‐grained image ranking. We propose to combine attribute‐based representation and online passive‐aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine‐grained image ranking, we introduce a supervised constrained clustering method to gather class‐balanced training instances for local PA‐based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state‐of‐the‐art methods in terms of accuracy and speed.

show abstract

Section: Related Workmentioning

confidence: 99%

PSI: A probabilistic semantic interpretable framework for fine‐grained image ranking

et al. 2018

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Also, person re-id is especially beneficial to multimedia content analysis. Examples include human movement analysis [9], clothing retrieval and recommendation [10], [11], finegrained object recognition [12]- [18], and so on. This is an inherently challenging problem because people appearance are intrinsically limited.…”

Section: Introductionmentioning

confidence: 99%

Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification

Wang

Gao

et al. 2019

IEEE Trans. Multimedia

223

111

View full text Add to dashboard Cite

Video-based person re-identification (re-id) is a central application in surveillance systems with significant concern in security. Matching persons across disjoint camera views in their video fragments is inherently challenging due to the large visual variations and uncontrolled frame rates. There are two steps crucial to person re-id, namely discriminative feature learning and metric learning. However, existing approaches consider the two steps independently, and they do not make full use of the temporal and spatial information in videos. In this paper, we propose a Siamese attention architecture that jointly learns spatiotemporal video representations and their similarity metrics. The network extracts local convolutional features from regions of each frame, and enhance their discriminative capability by focusing on distinct regions when measuring the similarity with another pedestrian video. The attention mechanism is embedded into spatial gated recurrent units to selectively propagate relevant features and memorize their spatial dependencies through the network. The model essentially learns which parts (where) from which frames (when) are relevant and distinctive for matching persons and attaches higher importance therein. The proposed Siamese model is end-to-end trainable to jointly learn comparable hidden representations for paired pedestrian videos and their similarity value. Extensive experiments on three benchmark datasets show the effectiveness of each component of the proposed deep network while outperforming state-of-the-art methods.

show abstract

“…F INE-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category, e.g., various species of birds [1], [2], [3], dogs [4], or flowers [5]. Owing to its importance in multimedia information retrieval [6], [7], [8], fine-grained object categorization has attracted widespread attention from multimedia community. However, the fine-grained categorization tasks are challenging because the variations among different subordinate object categories are subtle, which are often overwhelmed by those caused by arbitrary poses, viewpoint change, and/or occlusion.…”

Section: Introductionmentioning

confidence: 99%

Part-Aware Fine-Grained Object Categorization Using Weakly Supervised Part Detection Network

Zhang

Jia

Wang

2020

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. It is a rapidly developing subfield in multimedia content analysis. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories are subtle. It is well established that characterizing features of different subordinate categories are located on local parts of object instances. In fact, careful part annotations are available in many fine-grained categorization datasets. However, manually annotating object parts requires expertise, which is also difficult to generalize to new fine-grained categorization tasks. In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization. A vanilla PartNet builds on top of a base subnetwork two parallel streams of upper network layers, which respectively compute scores of classification probabilities (over subordinate categories) and detection probabilities (over a specified number of discriminative part detectors) for local regions of interest (RoIs). The imagelevel prediction is obtained by aggregating element-wise products of these region-level probabilities. To generate a diverse set of RoIs as inputs of PartNet, we propose a simple Discretized Part Proposals module (DPP) that directly targets for proposing candidates of discriminative local parts, with no bridging via object-level proposals. Experiments on the benchmark CUB-200-2011 and Oxford Flower 102 datasets show the efficacy of our proposed method for both discriminative part detection and finegrained categorization. In particular, we achieve the new stateof-the-art performance on CUB-200-2011 dataset when groundtruth part annotations are not available.

show abstract

Personal Clothing Retrieval on Photo Collections by Color and Attributes

Cited by 52 publications

References 33 publications

PSI: A probabilistic semantic interpretable framework for fine‐grained image ranking

PSI: A probabilistic semantic interpretable framework for fine‐grained image ranking

Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification

Part-Aware Fine-Grained Object Categorization Using Weakly Supervised Part Detection Network

Contact Info

Product

Resources

About