Representing local binary descriptors with BossaNova for visual recognition

Caetano, Carlos; Avila, Sandra; Guimarães, Silvio Jamil Ferzoli; Araújo, Arnaldo de Albuquerque

doi:10.1145/2554850.2555058

Cited by 14 publications

(23 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Figure 4, we illustrate the overview of our experimental approach. Note that this methodology is adapted from [4,17], in which the binary image descriptors are computed followed by the computation of a mid-level representation for each keyframe. Our proposed video descriptors are computed according to Equation 3 for representing each video that will be classified.…”

Section: Methodsmentioning

confidence: 99%

“…FREAK binary descriptors are encoded by a BoW representation and a SVM classifier is applied to recognition. We also explored in [17] the benefit of using several binary descriptors and mid-level representation for videos to identify pornographic content with a SVM classifier and a majority voting scheme.…”

Section: Local Features and Bag-of-words Based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A mid-level video representation based on binary descriptors: A case study for pornography detection

et al. 2016

Self Cite

View full text Add to dashboard Cite

With the growing amount of inappropriate content on the Internet, such as pornography, arises the need to detect and filter such material. The reason for this is given by the fact that such content is often prohibited in certain environments (e.g., schools and workplaces) or for certain publics (e.g., children). In recent years, many works have been mainly focused on detecting pornographic images and videos based on visual content, particularly on the detection of skin color. Although these approaches provide good results, they generally have the disadvantage of a high false positive rate since not all images with large areas of skin exposure are necessarily pornographic images, such as people wearing swimsuits or images related to sports. Local feature based approaches with Bag-of-Words models (BoW) have been successfully applied to visual recognition tasks in the context of pornography detection. Even though existing methods provide promising results, they use local feature descriptors that require a high computational processing time yielding high-dimensional vectors. In this work, we propose an approach for pornography detection based on local binary feature extraction and BossaNova image representation, a BoW model extension that preserves more richly the visual information. Moreover, we propose two approaches for video description based on the combination of mid-level representations namely BossaNova Video Descriptor (BNVD) and BoW Video Descriptor (BoW-VD). The proposed techniques are promising, achieving an accuracy of 92.40%, thus reducing the classification error by 16% over the current state-of-the-art local features approach on the Pornography dataset.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Local Features and Bag-of-words Based Approachesmentioning

confidence: 99%

A mid-level video representation based on binary descriptors: A case study for pornography detection

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, BossaNova Video Descriptor was adopted using SIFT to represent the video frames. We kept the BossaNova parameter values the same as in [4,5] (B = 2, λ min = 0.4, λ max = 2.0, s = 10 −3 , M = 1024) and we used two aggregating functions: max and median. For each video class, BNVDs generated are stored in a Slim-Tree [15] used for similarity searches.…”

Section: Methodsmentioning

confidence: 99%

Video similarity search by using compact representations

Silva

Almeida

Fonseca

et al. 2016

Proceedings of the 31st Annual ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

The amount of applications using unstructured data, like videos, has been increased, and the researches concerning multimedia retrieval have attracted great attention. The need to efficiently index and retrieve this kind of data is of great concern, due to the fact that common searching approaches based on the use of keywords are not adequate for large video databases. Similarity search is a content based approach and it has been successfully used in retrieval systems. Accordingly, a major challenge is to provide an accurate and compact video representation that can achieve good performance with a fast answer in this type of searching. In this work, we proposed a compact video representation by using Min-Hash and the k-nearest GIST descriptors. Furthermore, we also present the first use of BossaNova Video Descriptor (BNVD) to video similarity search. Both compact video representations have shown more than 88% of mean average precision on similarity video search. The experimental results indicate high efficiency of our proposed representations in video retrieval task.

show abstract

“…To the best of our knowledge, this outperforms the best results published on this dataset for this type of features. For instance, Caetano et al [6] reports a performance of 36.2% mAP with a model [4] computed based on a vocabulary of 1024 codewords, spatial pyramids and non-linear SVMs.…”

Section: Pascal Voc 2007mentioning

confidence: 99%

“…More related to our work, Uchida and Sakazawa [42] derived a FV based on mixtures of Bernoulli pdfs which was shown to perform better than the BoBW in an object retrieval task. In [6], the authors propose a model that extends the BoVW by computing histograms of distances between the set of descriptors and each element in the codebook, learned using the k-medians algorithm and the Hamming distance.…”

Section: Introductionmentioning

confidence: 99%

Exponential family Fisher vector for image classification

Sánchez

Redolfi

2015

Pattern Recognition Letters

View full text Add to dashboard Cite

a b s t r a c tOne of the fundamental problems in image classification is to devise models that allow us to relate the images to higher-level semantic concepts in an efficient and reliable way. A widely used approach consists on extracting local descriptors from the images and to summarize them into an image-level representation. Within this framework, the Fisher vector (FV) is one of the most robust signatures to date. In the FV, local descriptors are modeled as samples drawn from a mixture of Gaussian pdfs. An image is represented by a gradient vector characterizing the distributions of samples w.r.t. the model. Equipped with robust features like SIFT, the FV has shown state-of-the-art performance on different recognition problems. However, it is not clear how it should be applied when the feature space is clearly non-Euclidean, leading to heuristics that ignore the underlying structure of the space. In this paper we generalize the Gaussian FV to a broader family of distributions known as the exponential family. The model, termed exponential family Fisher vectors (eFV), provides a unified framework from which rich and powerful representations can be derived. Experimental results show the generality and flexibility of our approach.

show abstract

Representing local binary descriptors with BossaNova for visual recognition

Cited by 14 publications

References 25 publications

A mid-level video representation based on binary descriptors: A case study for pornography detection

A mid-level video representation based on binary descriptors: A case study for pornography detection

Video similarity search by using compact representations

Exponential family Fisher vector for image classification

Contact Info

Product

Resources

About