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Tattoo-ID: Automatic Tattoo Image Retrieval for Suspect and Victim Identification

Tattoo-ID: Automatic Tattoo Image Retrieval for Suspect and Victim Identification,10.1007/978-3-540-77255-2_28,Anil K. Jain,Jung-eun Lee,Rong Jin

Tattoo-ID: Automatic Tattoo Image Retrieval for Suspect and Victim Identification   (Citations: 3)
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Tattoos are used by law enforcement agencies for identification of a victim or a suspect using a false identity. Current method for matching tattoos is based on human-assigned class labels that is time consuming, subjective and has limited performance. It is desirable to build a content-based image retrieval (CBIR) system for automatic matching and retrieval of tattoos. We examine several key design issues related to building a prototype CBIR system for tattoo image database. Our system computes the similarity between the query and stored tattoos based on image content to retrieve the most similar tattoos. The performance of the system is evaluated on a database of 2,157 tattoos representing 20 different classes. Effects of segmentation errors, image transformations (e.g., blurring, illumination), influence of semantic labels and relevance feedback are also studied.
Conference: IEEE Pacific Rim Conference on Multimedia , pp. 256-265, 2007
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    • ...Our previous work used lowlevel image features (color, shape and texture) to represent and match tattoo images [34]...
    • ...Fig. 11 shows the retrieval performances based on SIFT features proposed here and low-level image features (color, shape, and texture) reported in [34]...
    • ...The corresponding accuracies reported in [34] are ~68.6% and ~82.5%, respectively...
    • ...Note that the experiments in [34] were conducted on only a subset (approximately half) of the database used here...
    • ...SIFT features are particularly effective in processing query images that are blurred and have uneven illumination, which were found to be the most difficult cases for the system in [34]...
    • ...For example, only 67% of blurred and 42% of illumination change queries appeared in top-20 retrievals in [34], but using SIFT features, these accuracies were increased to 94% and 92%, respectively...

    Jung-Eun Leeet al. Scars, marks and tattoos (SMT): Soft biometric for suspect and victim ...

    • ...The concept of visual similarity is crucial in many real applications like “tattoo image retrieval” for suspect or victim identification [15] that plays an important role in forensic and law enforcement...
    • ...Jain et al. proposed a CBIR system for tattoo image matching and retrieval [15]...
    • ...We use the same tattoo database as in [15], which contains 2,157 tattoo images downloaded from the web [2] and belonging to eight main classes and 20 subclasses in the ANSI/NIST standard [3]...
    • ...To simulate the various imaging conditions, we follow the work in [15] and generate 20 transformed images for every tattoo image in the database (see Figure 4). This results in a total of 43,140 synthesized images...
    • ...We choose the low level image attributes same as in [15], i.e., color, shape and texture...
    • ...The histogram intersection based approach used in [15] to measure image similarity, is used here as the baseline Figure 3. Eight different images of a butterfly tattoo taken under different imaging conditions...

    Jung-eun Leeet al. Rank-based distance metric learning: An application to image retrieval

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