Large-scale Image Retrieval System Research Articles

Duplicate Image Representation Based on Semi-Supervised Learning

For duplicate image detection, the more advanced large-scale image retrieval systems in recent years have mainly used the Bag-of-Feature ( BoF ) model to meet the real-time. However, due to the lack of semantic information in the training process of the visual dictionary, BoF model cannot guarantee semantic similarity. Therefore, this paper proposes a duplicate image representation algorithm based on semi-supervised learning. This algorithm first generates semi-supervised hashes, and then maps the image local descriptors to binary codes based on semi-supervised learning. Finally, an image is represented by a frequency histogram of binary codes. Since the semantic information can be effectively introduced through the construction of the marker matrix and the classification matrix during the training process, semi-supervised learning can not only guarantee the metric similarity of the local descriptors, but also guarantee the semantic similarity. And the experimental results also show this algorithm has a better retrieval effect compared with traditional algorithms.

A Filter of Minhash for Image Similarity Measures

Image similarity measures play an important role in nearest neighbor search and duplicate detection for large-scale image datasets. Recently, Minwise Hashing (or Minhash) and its related hashing algorithms have achieved great performances in large-scale image retrieval systems. However, there are a large number of comparisons for image pairs in these applications, which may spend a lot of computation time and affect the performance. In order to quickly obtain the pairwise images that theirs similarities are higher than the specific thresholdT(e.g., 0.5), we propose a dynamic threshold filter of Minwise Hashing for image similarity measures. It greatly reduces the calculation time by terminating the unnecessary comparisons in advance. We also find that the filter can be extended to other hashing algorithms, on when the estimator satisfies the binomial distribution, such as b-Bit Minwise Hashing, One Permutation Hashing, etc. In this pager, we use the Bag-of-Visual-Words (BoVW) model based on the Scale Invariant Feature Transform (SIFT) to represent the image features. We have proved that the filter is correct and effective through the experiment on real image datasets.

A Hybrid Local and Server-Based Large Scale Image Retrieval System and its Applications

A Hybrid Local and Server-Based Large Scale Image Retrieval System and its Applications

PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems

Dimensionality reduction in large-scale image research plays an important role for their performance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT) features and Speeded Up Robust Features (SURF) are extracted as image features. Second, the PCA is applied to reduce the dimensions of SIFT and SURF feature descriptors. By comparing multiple sets of experimental data with different image databases, we have concluded that PCA with a reduction in the range, can effectively reduce the computational cost of image features, and maintain the high retrieval performance as well

Local consistent hierarchical Hough Match for image re-ranking

Geometric image re-ranking is a widely adopted phrase to refine the large-scale image retrieval systems built based upon popular paradigms such as Bag-of-Words (BoW) model. Its main idea can be treated as a sort of geometric verification targeting at reordering the initial returning list by previous similarity ranking metrics, e.g. Cosine distance over the BoW vectors between query image and reference ones. In the literature, to guarantee the re-ranking accuracy, most existing schemes requires the initial retrieval to be conducted by using a large vocabulary (codebook), corresponding to a high-dimensional BoW vector. However, in many emerging applications such as mobile visual search and massive-scale retrieval, the retrieval has to be conducted by using a compact BoW vector to accomplish the memory or time requirement. In these scenarios, the traditional re-ranking paradigms are questionable and new algorithms are urgently demanded. In this paper, we propose an accurate yet efficient image re-ranking algorithm specific for small vocabulary in aforementioned scenarios. Our idea is inspired by Hough Voting in the transformation space, where votes come from local feature matches. Most notably, this geometry re-ranking can easily been aggregated to the cutting-edge image based retrieval systems yielding superior performance with a small vocabulary and being able to store in mobile end facilitating mobile visual search systems. We further prove that its time complexity is linear in terms of the re-ranking instance, which is a significant advantage over the existing scheme. In terms of mean Average Precision, we show that its performance is comparable or in some cases better than the state-of-the-art re-ranking schemes.

端末とサーバによるハイブリッド大規模画像検索システムとその応用

著者らは実空間とWeb空間との連動を目的とし，TV CMや屋外広告等をスマートフォンで画像認識するアプリを開発した．本技術により，スマートフォンをかざす対象，場所，連動ゲームのスコアに応じて異なる関連情報を提示することでユーザに新たな体験を提供するとともに，放送と通信とにおけるマルチスクリーンのシームレスな連携を実現した．事前学習した特徴量を1/10以下に圧縮し，圧縮状態のまま認識することでスマートフォンでもリアルタイム動作を実現し，認識後に起動するゲームではTV CMと同じくスマートフォンに閉じ込められている男女を再会させる演出で，認識対象等に応じたマルチストーリー分岐を用意した．本稿では，全国放映されたTV CVでも多くのユーザが同時利用できる大規模画像検索技術と，高臨場感を醸し出す背景領域抽出技術，および当該アプリを一般公開した効果や反響について報告する．

SIFT match verification by geometric coding for large-scale partial-duplicate web image search

Most large-scale image retrieval systems are based on the bag-of-visual-words model. However, the traditional bag-of-visual-words model does not capture the geometric context among local features in images well, which plays an important role in image retrieval. In order to fully explore geometric context of all visual words in images, efficient global geometric verification methods have been attracting lots of attention. Unfortunately, current existing methods on global geometric verification are either computationally expensive to ensure real-time response, or cannot handle rotation well. To solve the preceding problems, in this article, we propose a novel geometric coding algorithm, to encode the spatial context among local features for large-scale partial-duplicate Web image retrieval. Our geometric coding consists of geometric square coding and geometric fan coding, which describe the spatial relationships of SIFT features into three geo-maps for global verification to remove geometrically inconsistent SIFT matches. Our approach is not only computationally efficient, but also effective in detecting partial-duplicate images with rotation, scale changes, partial-occlusion, and background clutter. Experiments in partial-duplicate Web image search, using two datasets with one million Web images as distractors, reveal that our approach outperforms the baseline bag-of-visual-words approach even following a RANSAC verification in mean average precision. Besides, our approach achieves comparable performance to other state-of-the-art global geometric verification methods, for example, spatial coding scheme, but is more computationally efficient.