Compact feature based clustering for large-scale image retrieval

Liang, Yan; Dong, Le; Xie, Shanshan; Lv, Na; Xu, Zongyi

doi:10.1109/icmew.2014.6890597

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…Nevertheless, compared with BOW representation, the technique reduces the retrieval accuracy and still requires tremendous memory per image. In [39], compact feature based clustering was proposed to represent images by aggregating clustering centers and statistics of SIFT features assigned to each clusters. Although it reduces the computation and memory footprint to some extent, it still needs several feature vectors to describe images.…”

Section: Related Workmentioning

confidence: 99%

Holons Visual Representation for Image Retrieval

Dong

Liang

Kong

et al. 2016

IEEE Trans. Multimedia

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Along with the enlargement of image scale, convolutional local features, such as SIFT, are ineffective for representing or indexing and more compact visual representations are required. Due to the intrinsic mechanism, state-of-the-art Vector of Locally Aggregated Descriptors (VLAD) has a few limits. Based on this, we propose a new descriptor named Holons Visual Representation (HVR). The proposed HVR is a derivative mutational self-contained combination of global and local information. It exploits both global characteristics and the statistic information of local descriptors in the image dataset. It also takes advantages of local features of each image and computes their distribution with respect to the entire local descriptor space. Accordingly, the HVR is computed by a twolayer hierarchical scheme, which splits the local feature space and obtains raw partitions, as well as, the corresponding refined partitions. Then according to the distances from the centroids of partition spaces to local features and their spatial correlation, we assign the local features into their nearest raw partitions and refined partitions to obtain the global description of an image. Compared with VLAD, HVR holds critical structure information and enhances the discriminative power of individual representation with a small amount of computation cost, while using the same memory overhead. Extensive experiments on several benchmark datasets demonstrate that the proposed HVR outperforms conventional approaches in terms of scalability as well as retrieval accuracy for images with similar intra local information.

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Section: Related Workmentioning

confidence: 99%

Holons Visual Representation for Image Retrieval

Dong

Liang

Kong

et al. 2016

IEEE Trans. Multimedia

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“…On the other hand, [49] proposes a compact feature based clustering (CFC) to represent images. [47] improves the retrieval performance by automatically learning robust visual features and hash functions.…”

Section: Related Workmentioning

confidence: 99%

Coarse2Fine: Two-Layer Fusion For Image Retrieval

Kong,

Dong,

Dong

et al. 2016

Preprint

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This paper addresses the problem of large-scale image retrieval. We propose a two-layer fusion method which takes advantage of global and local cues and ranks database images from coarse to fine (C2F). Departing from the previous methods fusing multiple image descriptors simultaneously, C2F is featured by a layered procedure composed by filtering and refining. In particular, C2F consists of three components. 1) Distractor filtering. With holistic representations, noise images are filtered out from the database, so the number of candidate images to be used for comparison with the query can be greatly reduced. 2) Adaptive weighting. For a certain query, the similarity of candidate images can be estimated by holistic similarity scores in complementary to the local ones. 3) Candidate refining. Accurate retrieval is conducted via local features, combining the pre-computed adaptive weights. Experiments are presented on two benchmarks, i.e., Holidays and Ukbench datasets. We show that our method outperforms recent fusion methods in terms of storage consumption and computation complexity, and that the accuracy is competitive to the state-of-the-arts.

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“…ASBO-based document clustering has been presented by Prakasha et al (2013). Problem related with similar image retrieval from large data base has been presented by Liang et al (2014), for fast similar image retrieval, especially for large-scale datasets with millions of images. SA-based clustering search algorithm for the rank aggregation problem has presented by Lorena et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Cluster optimisation in information retrieval using self-exploration-based PSO

Prakasha¹,

Raju²,

Singh³

2016

IJIEI

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Self-exploration capability is an important and necessary factor in all social communities where individual assumes to have their own intelligence. Macro social influencing factors are responsible for decision nature taken by an individual, whereas self-exploration process can be considered as a refinement of that decision by use of the cognitive capability to explore a number of surrounding possibilities. The mathematical model corresponding to the individual self-exploration process can be expressed with the help of the chaotic search method. In this paper, chaotic search-based self-exploration has integrated with social influenced-based particle swarm optimisation (PSO) to represent better computational model so that the complex optimisation problem could solve more efficiently. Two different levels of self-exploration called intrinsic cascade self-exploration and extrinsic cascade self-exploration have applied in association with PSO. This paper has applied the proposed concept to cluster documents data in the area of information retrieval and to achieve the global solutions for high dimensional numerical optimisation problems.

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Compact feature based clustering for large-scale image retrieval

Cited by 6 publications

References 21 publications

Holons Visual Representation for Image Retrieval

Holons Visual Representation for Image Retrieval

Coarse2Fine: Two-Layer Fusion For Image Retrieval

Cluster optimisation in information retrieval using self-exploration-based PSO

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