Recent Advance in Content-based Image Retrieval: A Literature Survey (1706.06064v2)

Abstract: The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.

• The paper surveys comprehensive CBIR advancements, transitioning from handcrafted features like SIFT to learning-based deep CNN models.
• It details novel methodologies including visual codebook learning, spatial context embedding, and efficient database indexing.
• The survey outlines future research directions focused on improved query formation, cross-modal retrieval, and real-world CBIR applications.

Content-based Image Retrieval: Recent Advances and Future Directions

The paper "Recent Advance in Content-based Image Retrieval: A Literature Survey" by Wengang Zhou, Houqiang Li, and Qi Tian offers a comprehensive survey of advancements in content-based image retrieval (CBIR) between 2003 and 2016. The rapid increase in digital imaging and its widespread accessibility have intensified the need for effective image retrieval systems that rely on visual content features rather than solely on accompanying textual metadata. This shift is crucial as traditional text-based methods can suffer from inconsistencies between text descriptions and visual content.

Key Challenges in CBIR

CBIR faces two primary challenges: the intention gap and the semantic gap. The intention gap pertains to the difficulty users encounter in precisely expressing their query intentions using visual inputs. The semantic gap refers to the challenge of bridging the low-level visual features with high-level semantic concepts. To address these issues, the research community has developed various strategies, particularly focusing on the following:

1. Feature Extraction: The field has evolved from handcrafted features like SIFT and SURF to more recent learning-based features using deep convolutional neural networks (CNNs), which provide semantic-rich representations.
2. Visual Codebook Learning and Feature Quantization: Techniques such as k-means clustering and product quantization have been used to reduce the dimensionality of visual features, allowing for scalable image indexing.
3. Spatial Context Embedding and Image Representation: To improve the distinctiveness of feature representations, spatial context integration methods have been introduced, which augment the visual bag-of-features models with various geometric and contextual information.
4. Database Indexing Techniques: Inverted file indexing and hashing-based methods are explored to efficiently retrieve images by organizing image representations for faster query responses.
5. Image Scoring and Reranking: Algorithms have been devised to calculate similarity scores and optimize result ranking based on feature matches and contextual information, often utilizing geometric consistency checks and query expansion methods to refine search outcomes.

Implications and Future Research Directions

The paper surmises that despite major strides, significant advancements are still necessary to achieve truly semantic-aware CBIR systems. The following potential directions are outlined for future research:

• Development of Comprehensive Datasets: Larger and more specific datasets are needed to better evaluate and improve CBIR systems.
• Enhancement in Query Formation: Novel user interfaces and AI-driven tools for better capturing user intent could greatly improve initial query formulations.
• Incorporation of Deep Learning: Leveraging advances in deep learning, particularly CNNs and deep hash functions, could enable more efficient and semantically meaningful image representations.
• Cross-modal Retrieval: Integrating multiple data modalities like text, audio, and visual data can enhance retrieval effectiveness by fusing complementary information sources.
• Real-world Applications and Benchmarking: Increased collaboration between academia and industry through challenges and real-world application benchmarks could foster the adoption of robust CBIR solutions.

The paper provides a detailed roadmap for the ongoing evolution of CBIR technologies and encourages ongoing exploration in multi-modal retrieval and subtle user interaction methodologies to bridge the gaps remaining in content-based image retrieval. These advancements hold the potential to significantly impact practical applications, including e-commerce, digital asset management, and social media analytics.