Clothing Co-Parsing by Joint Image Segmentation and Labeling (1502.00739v1)

Abstract: This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (E-SVM) technique [23]. In the second phase (i.e. "region co-labeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.

• The paper introduces a dual-phase approach that first uses image co-segmentation with exemplar-SVM and then applies multi-image graphical modeling for co-labeling.
• It achieves segmentation accuracies of 90.29% on the Fashionista dataset and 88.23% on the CCP dataset, outperforming conventional methods.
• The research demonstrates a scalable technique for precise clothing recognition in e-commerce by leveraging image-level tags for refined segmentation and labeling.

Clothing Co-Parsing by Joint Image Segmentation and Labeling

The paper "Clothing Co-Parsing by Joint Image Segmentation and Labeling" proposes a sophisticated data-driven framework that addresses the problem of clothing image parsing. The primary goal is to effectively segment and label regions within clothing images using a novel co-parsing approach, leveraging the potential of image-level tags to guide the pixelwise annotation process. This approach is relevant to applications in internet-based e-commerce where accurate clothing recognition and retrieval are critical.

Framework Overview

The proposed framework functions in two phases: image co-segmentation and region co-labeling. The initial phase, image co-segmentation, involves iterative refinement of image regions by employing the exemplar-SVM (E-SVM) technique. The method begins with the extraction of superpixels, followed by their grouping into regions. From these regions, a set of coherent regions is selected to train E-SVM classifiers. These classifiers then serve as templates to propagate segmentations across all images, thereby enhancing the consistency of region segmentation throughout the image set.

In the subsequent phase of region co-labeling, a multi-image graphical model is constructed. Here, the previously segmented regions act as vertices in the graph, integrating various contexts such as item location and garment interactions. The joint label assignment problem is framed as a Graph Cuts optimization task, which efficiently incorporates these contextual clues to achieve superior labeling performance.

Experimental Validation

The authors evaluate their framework on two datasets: the Fashionista dataset and a newly constructed Clothing Co-Parsing (CCP) dataset. The CCP dataset consists of 2098 high-resolution street fashion photos, providing a challenging testing ground with diverse backgrounds and poses. The proposed method achieves segmentation accuracies of 90.29% on Fashionista and 88.23% on CCP, with corresponding recognition rates of 65.52% and 63.89%, respectively. These results demonstrate a clear advantage over existing methods, including PECS, BSC, and STF, particularly highlighting the benefit of the co-parsing strategy when dealing with complex clothing images.

Discussion and Future Directions

The implications of this research are significant in both theoretical and practical domains. The integration of E-SVM within the co-segmentation process and the subsequent multi-image graphical model for co-labeling introduce an effective means of tackling clothing parsing, which can be extended to other domains requiring fine-grained image understanding. The ability to use image-level tags to inform pixelwise segmentation offers a scalable approach that reduces annotation overhead.

Future research directions could explore the iterative interaction between the co-segmentation and co-labeling phases to refine results further. Additionally, the framework might benefit from advancements in computational efficiency, potentially through parallel processing methods, to better accommodate large-scale deployments. The extension of this approach to adapt to evolving fashion trends and accommodate dynamic datasets would further enhance its applicability in e-commerce ecosystems.

In conclusion, this paper presents a well-structured approach to clothing image parsing, employing a robust combination of image segmentation techniques and contextual modeling to achieve state-of-the-art results. The novel methodology and the significant improvements in performance underscore the potential of the framework to advance the field of computer vision in fashion-related applications.