Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval (1501.06272v2)

Abstract: With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels. However, most of these hashing methods are designed to handle simple binary similarity. The complex multilevel semantic structure of images associated with multiple labels have not yet been well explored. Here we propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images. In our approach, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features. Meanwhile, a ranking list that encodes the multilevel similarity information is employed to guide the learning of such deep hash functions. An effective scheme based on surrogate loss is used to solve the intractable optimization problem of nonsmooth and multivariate ranking measures involved in the learning procedure. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods in term of ranking evaluation metrics when tested on multi-label image datasets.

Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval

The paper, "Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval," addresses the challenge of efficiently retrieving multi-label images from large-scale datasets by proposing a deep semantic ranking approach. This work leverages deep convolutional neural networks (CNNs) to learn hash functions that encode complex multilevel semantic structures inherent in multi-label datasets, offering significant improvements over traditional binary hashing methods.

Core Contributions

1. Semantic Ranking Integration: The authors propose a novel framework that integrates semantic ranking information directly into the hash function learning process. Unlike earlier methods focusing on binary similarity, this approach addresses the nuances of multilevel similarity by considering varying degrees of label overlap in multi-label images.
2. Deep Hash Function Design: This paper introduces the utilization of deep CNNs for constructing hash functions. By learning directly from images, the network surpasses the limits of hand-crafted features, capturing richer semantic representations.
3. Optimization with Surrogate Loss: The work utilizes a ranking loss based on triplets to manage the complexities associated with nonsmooth and multivariate ranking measures, making the training process tractable using stochastic gradient descent.

Methodological Insights

The framework incorporates CNNs as the backbone, learning both feature representation and hash mappings from raw image data. This end-to-end approach contrasts with conventional pipelines that depend on pre-extracted features, allowing the simultaneous optimization of representation quality and hashing performance.

The semantic ranking supervision utilizes ground-truth rankings derived from label overlaps, ensuring that the hierarchical structure of semantic information is preserved in the learned binary codes. An adaptive weighted loss is applied to prioritize the ranking accuracy of items that are semantically closer, aligning with practical retrieval needs where users focus on top-ranked results.

Experimental Evaluation

The approach is evaluated on benchmark datasets MIRFLICKR-25K and NUS-WIDE, demonstrating substantial improvements over existing methods. Metrics like NDCG, ACG, and weighted mAP confirm that the proposed method outperforms traditional hashing techniques, superiorly preserving the multilevel semantic structure.

Key experimental results indicate:

• Significant enhancements in image retrieval performance when using deep semantic ranking compared to both data-independent and data-dependent hash methods.
• The superiority of integrated deep learning models over shallow, hand-crafted feature-based approaches.
• Robustness in maintaining performance across different bit lengths, showing adaptability to varying computational constraints.

Implications and Future Directions

The introduction of deep semantic ranking for hashing functions represents a noteworthy advancement in image retrieval systems. By aligning hashing with hierarchical semantic similarities, this work lays the groundwork for more effective retrieval mechanisms that can be extended to other domains such as video retrieval and multimodal data.

Future directions may include exploring alternative loss functions that capture even finer semantic variations or extending the model to incorporate unsupervised or semi-supervised learning paradigms to further enhance the scalability and applicability in diverse environments.

In summary, the paper offers a compelling argument for utilizing deep learning with semantic ranking to effectively handle the complexities of multi-label image retrieval, setting a new benchmark for future research in this area.