Partial-duplicate Image Retrieval Research Articles

Evolved Hierarchical Masking for Self-Supervised Learning.

Existing Masked Image Modeling methods apply fixed mask patterns to guide the self-supervised training. As those mask patterns resort to different criteria to depict image contents, sticking to a fixed pattern leads to a limited vision cues modeling capability. This paper introduces an evolved hierarchical masking method to pursue general visual cues modeling in self-supervised learning. The proposed method leverages the vision model being trained to parse the input visual cues into a hierarchy structure, which is hence adopted to generate masks accordingly. The accuracy of hierarchy is on par with the capability of the model being trained, leading to evolved mask patterns at different training stages. Initially, generated masks focus on low-level visual cues to grasp basic textures, then gradually evolve to depict higher-level cues to reinforce the learning of more complicated object semantics and contexts. Our method does not require extra pre-trained models or annotations and ensures training efficiency by evolving the training difficulty. We conduct extensive experiments on seven downstream tasks including partial-duplicate image retrieval relying on low-level details, as well as image classification and semantic segmentation that require semantic parsing capability. Experimental results demonstrate that it substantially boosts performance across these tasks. For instance, it surpasses the recent MAE by 1.1% in imageNet-1K classification and 1.4% in ADE20K segmentation with the same training epochs. We also align the proposed method with the current research focus on LLMs. The proposed approach bridges the gap with large-scale pre-training on semantic demanding tasks and enhances intricate detail perception in tasks requiring low-level feature recognition.

Partial-duplicate image retrieval using spatial and visual contextual clues

The traditional bag-of-visual-words (BOW) model quantifies the local features to the visual words to achieve efficient content-based image retrieval. However, since it causes considerable quantisation error and ignores the spatial relationships between visual words, the accuracy of partial-duplicate image retrieval based on BOW model is limited. In order to reduce the quantisation error and improve the discriminability of visual words, many partial-duplicate image retrieval methods have been proposed, which make use of the advantages of the geometric clues between visual words. In this paper, we propose a novel partial-duplicate scheme by using both spatial and visual contextual clues for removing the false matches effectively, which not only encodes the relationships of orientation, distance and dominant orientation between the referential visual word and its context, but also takes the colour information between visual words into consideration. Experimental results reveal that our proposed algorithm achieves superior performance to the state-of-the-art methods for partial-duplicate image retrieval.

Partial-duplicate image retrieval based on HSV colour space for coverless information hiding

In conventional image steganography approaches, modification traces will be left in the designated cover images. Consequently, the secret information could be possibly detected by steganalysis tools. In this paper, a coverless image steganography approach based on partial-duplicate image retrieval is proposed to hide images without modification. First, we divide equally any given query or database image into a set of image blocks. Then, for a given query image, we search for its partial-duplicate images in a large-scale database according to the similarity between the HSV colour features of image blocks. Finally, these retrieved partial-duplicate images are used to represent and hide the secret image. Moreover, we propose an encryption algorithm based on chaotic maps to ensure the security of our approach. Both the theoretical analysis and experimental results demonstrate that the proposed coverless image steganography approach has good security, high robustness and strong resistance to the existing steganalysis tools.

Visual vocabulary tree-based partial-duplicate image retrieval for coverless image steganography

Visual vocabulary tree-based partial-duplicate image retrieval for coverless image steganography

Visual vocabulary tree-based partial-duplicate image retrieval for coverless image steganography

The traditional image steganographic approaches embed the secret message into covers by modifying their contents. Therefore, the modification traces left in the cover will cause some damages to the cover, especially embedding more messages in the cover. More importantly, the modification traces make successful steganalysis possible. In this paper, visual vocabulary tree-based partial-duplicate image retrieval for coverless image steganography is proposed to embed the secret messages without any modification. The main idea of our method is to retrieve a set of duplicates of a given secret image as stego-images from a natural image database. The images in the database will be divided into a number of image patches, and then indexed by the features extracted from the image patches. We search for the duplicates of the secret image in the image database to obtain the stego-images. Each of these stego-images shares one similar image patch with the secret image. When receiver obtains those stego-images, our method can recover the secret image approximately by using the designed protocols. Experimental results show that our method not only resists the existing steganalysis tools, but also has high capacity.

Partial-duplicate image retrieval based on HSV colour space for coverless information hiding

Partial-duplicate image retrieval based on HSV colour space for coverless information hiding

Encoding multiple contextual clues for partial-duplicate image retrieval

Recently, most of partial-duplicate image retrieval approaches build on the bag-of-visual-words (BOW) model, in which local image features are quantized into a bag of compact visual words, i.e., BOW representation, for fast image matching. However, due to the quantization errors in visual words, the BOW representation shows low discriminability, which causes negative influence on retrieval accuracy. Encoding contextual clues into the BOW representation is a popular technique to improve its discriminability. Unfortunately, the captured contextual clues are generally not stable and informative enough, resulting in limited discriminability improvement. To address the issues, we propose a multiple contextual clue encoding approach for partial-duplicate image retrieval. By treating each visual word of any given query or database image as a center, we first propose an asymmetrical context selection strategy to select the contextual visual words for the query and database images differently. Then, we capture the multiple contextual clues: the geometric relationships, the visual relationships, and the spatial configurations between the center and its contextual visual words. These captured multiple contextual clues are compressed to generate the multi-contextual descriptors, which are further integrated with the center visual word to improve the discriminability of BOW representation. Experiments conducted on the large-scale partial-duplicate image dataset demonstrate that the proposed approach provides higher retrieval accuracy than the state-of-the-arts, while achieves comparable performances in time and space efficiency.

Fusing multi-cues description for partial-duplicate image retrieval

In traditional image retrieval, images are commonly represented using Bag-of-visual-Words (BoW) built from image local features. However, the lack of spatial and structural information suppresses its performance in applications. In this paper, we introduce a multi-cues description by fusing structural, content and spatial information for partial-duplicate image retrieval. Firstly, we propose a rotation-invariant Local Self-Similarity Descriptor (LSSD), which captures the internal structural layouts in the local textural self-similar regions around interest points. Then, based on the spatial pyramid model, we make use of both LSSD and SIFT to construct an image representation with multi-cues. Finally, we formulate the Semi-Relative Entropy as the distance metric. Comparison experiments with state-of-the-art methods on four popular databases show the efficiency and effectiveness of our approach.

Cross-Indexing of Binary SIFT Codes for Large-Scale Image Search

In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm.

Robust Spatial Consistency Graph Model for Partial Duplicate Image Retrieval

Partial duplicate images often have large non-duplicate regions and small duplicate regions with random rotation, which lead to the following problems: 1) large number of noisy features from the non-duplicate regions; 2) small number of representative features from the duplicate regions; 3) randomly rotated or deformed duplicate regions. These problems challenge many content based image retrieval (CBIR) approaches, since most of them cannot distinguish the representative features from a large proportion of noisy features in a rotation invariant way. In this paper, we propose a rotation invariant partial duplicate image retrieval (PDIR) approach, which effectively and efficiently retrieves the partial duplicate images by accurately matching the representative SIFT features. Our method is based on the Combined-Orientation-Position (COP) consistency graph model, which consists of the following two parts: 1) The COP consistency, which is a rotation invariant measurement of the relative spatial consistency among the candidate matches of SIFT features; it uses a coarse-to-fine family of evenly sectored polar coordinate systems to softly quantize and combine the orientations and positions of the SIFT features. 2) The consistency graph model, which robustly rejects the spatially inconsistent noisy features by effectively detecting the group of candidate feature matches with the largest average COP consistency. Extensive experiments on five large scale image data sets show promising retrieval performances.