Matching-CNN Meets KNN: Quasi-Parametric Human Parsing (1504.01220v1)

Abstract: Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts, for the human parsing task.

• The paper proposes a novel quasi-parametric human parsing model that combines a Matching Convolutional Neural Network (M-CNN) with a K Nearest Neighbor (KNN)-based retrieval system.
• This hybrid approach achieves significantly higher accuracy, demonstrated by an F1-score of 62.81% on a 7,700-image dataset, outperforming previous methods.
• The model offers improved adaptability to new image data and has potential applications in other exemplar-based computer vision tasks such as object detection and face parsing.

Quasi-Parametric Human Parsing: Integrating Matching-CNN and KNN

The paper presents a novel approach for human parsing, conceptualized as a quasi-parametric model that amalgamates the strengths of both parametric and non-parametric methodologies. Human parsing, the process of segmenting a human image into distinct semantic regions such as headgear, clothing, and limbs, is a complex task that has traditionally been approached using either parametric or non-parametric methods. Despite the promising performance of both, each methodology has inherent limitations. Parametric methods, while powerful, often lack flexibility and adaptability to novel data without comprehensive retraining. Conversely, non-parametric methods can flexibly incorporate new data but generally suffer from less accurate matching due to their reliance on heuristic label transfer from a manually annotated corpus.

To overcome these challenges, the authors propose a quasi-parametric framework, which leverages the robustness of a parametric approach via a Matching Convolutional Neural Network (M-CNN) alongside the flexibility of a non-parametric K Nearest Neighbor (KNN)-based retrieval system. In this framework, M-CNN assesses the matching confidence and displacement of regions between test images and their KNN matches, retrieved from an annotated human image corpus. This enables the precise transfer of semantic labels from KNN images to new data, thereby enhancing the parsing accuracy and adaptability of the model.

The architecture of M-CNN is particularly noteworthy as it incorporates cross-image matching filters across multiple convolutional layers. This design enables the model to effectively handle a broad range of spatial displacements, accommodating variations in the positioning of semantic regions like clothing and accessories across different images. The results from exhaustive experiments with a dataset containing 7,700 annotated human images demonstrate the model's superior performance, surpassing state-of-the-art methods such as those by Yamaguchi et al. (2012; 2013), as evidenced by higher accuracy and F1-scores across evaluations.

Symbolically, the M-CNN acts as a robust regressor, learning to predict not only whether a semantic label is present but also precisely mapping the label's spatial coordinates onto the test image. The integration of cross-image matching further refines this process, establishing a more nuanced understanding of spatial relationships within the dataset's imagery. The paper's results underscore the model's improved parsing quality, achieving a remarkable 62.81% in F1-score, significantly outperforming previous methods which reached approximately 44.76%.

The implications for this research are manifold. Practically, this approach could greatly enhance the applicability of human parsing in real-world scenarios where clothing styles and image contexts evolve rapidly, requiring models to expand their understanding dynamically and efficiently. Theoretically, the quasi-parametric approach presents a new paradigm in image parsing, suggesting that future models could benefit from similar hybrid frameworks that capitalize on both the structured learning of parametric models and the adaptability of non-parametric systems.

Looking ahead, there is considerable potential for this work to influence other domains of computer vision, notably in exemplar-based tasks such as object detection and face parsing. Moreover, the adoption of sophisticated network architectures like GoogLeNet could further amplify the capabilities of the proposed framework, suggesting a promising trajectory for future developments in image parsing and beyond.