23 research outputs found
Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval
To effectively retrieve objects from large corpus with high accuracy is a
challenge task. In this paper, we propose a method that propagates visual
feature level similarities on a Markov random field (MRF) to obtain a high
level correspondence in image space for image pairs. The proposed
correspondence between image pair reflects not only the similarity of low-level
visual features but also the relations built through other images in the
database and it can be easily integrated into the existing
bag-of-visual-words(BoW) based systems to reduce the missing rate. We evaluate
our method on the standard Oxford-5K, Oxford-105K and Paris-6K dataset. The
experiment results show that the proposed method significantly improves the
retrieval accuracy on three datasets and exceeds the current state-of-the-art
retrieval performance
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.Comment: 8 pages, 7 figures, 6 tables. Accepted to CVPR 201
A Brief Review On Image Retrieval Techniques and its Scope
This paper presents the novel approach for image retrieval. Image retrieval is an important problem in many applications, such as copyright infringement detection, tag annotation, commercial retrieval, and landmark identification. Image retrieval definition is given and the concept and significance of image retrieval is also provided. Various image retrieval techniques based on content based, sketch based, also based on image annotation is explained here. The last section includes the approach for retrieval is given as a problem formulation